Alumna: Erika Samara Alvares Angelim
Web Personal: https://www.erikaalvares.es/
Detección de fraudes con tarjetas de crédito¶
Enlace al dataset: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
Índice¶
- Detección de fraudes con tarjetas de crédito
- Índice
- Introducción
- Metodología
- Configuracion del Ambiente
- Exploración de Datos
- Visualización Inicial de Datos
- Preprocesamiento
- Limpieza de Datos
- Distribución de la variable "Class" antes del proceso de limpieza
- Valores perdidos
- Datos duplicados
- Corrección de Sesgos (Skewness) en Características Numéricas
- Detección de anomalías (método: Isolation Forest)
- Regenerar gráficas con datos limpios
- Eliminación de columnas irrelevantes
- Escalado de Características
- Limpieza de Datos
- Evaluación Comparativa de Modelos
- Separa el dataset
- Técnicas
- Técnica 1: Entrenamiento sobre el Dataset Original
- Técnica 2: Balanceado con SMOTE
- Técnica 3: Balanceado con RandomUnderSampler
- Técnica 4: Validación Cruzada de Mejores Modelos (Balanceado con SMOTE)
- Técnica 5: Validación Cruzada de Mejores Modelos (dataset original)
- Técnica 6: Ensembles con SMOTE
- Técnica 7: Ensembles con dataset original (clean_data)
- Tabla Resumen de Resultados
- Evaluar el impacto del tiempo de procesamiento con SMOTE
- Mejores Modelos con AUPRC > 80%
- Selección de Algoritmos/Modelos para ponerlo en producción
- Informe de Clasificación y Métricas
- Aplicación para probar el modelo con datos nuevos
- Conclusiones
- Código Completo para Generar el PDF Dinámico
- Exportar cuaderno a formato HTML y a formato PDF
Introducción¶
Visión general del proyecto: El proyecto está diseñado para dotar a los participantes de las habilidades y conocimientos necesarios para
utilizar eficazmente la IA en la detección de actividades fraudulentas en las transacciones con tarjetas de crédito.
🎯 Objetivo del Proyecto¶
Explorar el conjunto de datos
Realizar un análisis exploratorio de datos (EDA) para comprender la estructura del conjunto de datos, identificar las características principales y detectar patrones iniciales en las variables.Visualizar diferencias entre clases
Generar gráficos comparativos para identificar diferencias clave entre transacciones fraudulentas y no fraudulentas, enfocándose en distribuciones, valores atípicos y comportamientos específicos.Corregir el desequilibrio de clases
Aplicar técnicas de balanceo de datos para tratar el marcado desequilibrio de clases, asegurando que los modelos puedan aprender de manera efectiva y mejorar su capacidad de detección de fraudes.Desarrollar un modelo predictivo
Entrenar y evaluar modelos de Machine Learning con el objetivo de detectar fraudes en transacciones con alta precisión, priorizando un rendimiento robusto.Minimizar falsos positivos y negativos
Optimizar los modelos para reducir la tasa de falsos positivos (detectar fraudes donde no existen) y falsos negativos (no detectar fraudes reales), mitigando riesgos y costos asociados.Seleccionar el mejor modelo
Comparar el desempeño de los modelos utilizando métricas clave como AUPRC, Recall y FNR (Tasa de Falsos Negativos), para seleccionar el modelo con el mejor equilibrio entre precisión y capacidad de generalización.Proponer soluciones prácticas
Ofrecer un modelo implementable que permita la detección automática de fraudes, contribuyendo a la toma de decisiones informadas y a la reducción de pérdidas económicas.
Descripción del Problema¶
En la detección de fraudes con tarjetas de crédito, el principal desafío para las compañías es proteger a los clientes de cargos no autorizados y reducir pérdidas económicas 💸.
📊 Descripción del Conjunto de Datos
- Este conjunto de datos incluye transacciones anónimas realizadas en Europa en septiembre de 2013, etiquetadas como fraudulentas o genuinas (No-Fraudulentas).
- Dado que los fraudes representan solo un 0.172% del total de transacciones (492 de 284,807), el conjunto de datos está altamente desbalanceado, lo cual presenta un gran reto para los modelos de detección.
📋 Características de los Datos
- Componentes principales (V1, V2, … V28): Transformados usando PCA para preservar la confidencialidad.
- ⏰ Tiempo (Time): Segundos transcurridos desde la primera transacción.
- 💵 Cantidad (Amount): Monto (importe) de la transacción, útil para aprendizaje sensible al costo.
- 🎯 Clase (Class): Variable objetivo, donde 1 indica fraude y 0 indica transacción genuina.
🚀 Desafíos y Recomendaciones para abordar el problema:
Se recomienda usar métricas especializadas como el Área Bajo la Curva de precisión-recuperación (Precision-Recall: AUPRC/AUC-PR), ya que la precisión de la matriz de confusión no es significativa en conjuntos de datos desequilibrados.
La métrica UPRC/AUC-PR es usada en escenarios como la detección de fraudes. El Recall es crucial porque queremos minimizar los casos en los que no detectamos un fraude (Falsos Negativos).
Un Precision alto también es importante para evitar clasificar transacciones legítimas como fraudulentas (Falsos Positivos).Los modelos deben ser capaces de diferenciar eficazmente entre clases minoritarias y mayoritarias, asegurando una detección fiable sin comprometer la precisión en casos de transacciones genuinas.
Este enfoque permite una detección efectiva de fraudes, protegiendo tanto a los consumidores como a las empresas de transacciones no autorizadas.
Metodología¶
Explicación de técnicas, modelos evaluados, y manejo del desbalance.
Tecnica: Organización y Propósito
En este proyecto, las etapas de preprocesamiento y entrenamiento se han estructurado en múltiples técnicas para evaluar de manera sistemática cómo diferentes métodos impactan el rendimiento de los modelos en la detección de fraudes. Este enfoque organizado permite comparar de manera objetiva los resultados obtenidos y seleccionar la combinación más efectiva de técnicas y algoritmos de machine learning.
El proceso se desarrolla en varias fases:
Evaluación inicial de técnicas:
- Se utilizan diversas técnicas de preprocesamiento, incluyendo balanceo de clases (Oversampling, Undersampling y SMOTE), y modelos.
- Los resultados obtenidos se analizan para identificar los algoritmos y técnicas que producen el mejor desempeño según métricas clave, como AUPRC para datasets desbalanceados.
Selección de los mejores modelos:
- Con base en los resultados de las técnicas, se seleccionan los modelos que obtienen los mejores valores en las métricas evaluadas.
Validación Cruzada:
- Los modelos seleccionados se someten a una técnica adicional que implementa validación cruzada. Esto asegura que el rendimiento del modelo es consistente y no depende de divisiones específicas de los datos.
Técnicas de Ensemble:
- Se aplican métodos avanzados como VotingClassifier y StackingClassifier para combinar los modelos seleccionados.
- VotingClassifier combina modelos mediante votación (mayoritaria o promedio ponderado), lo que refuerza la robustez del resultado final.
- StackingClassifier utiliza un modelo meta para aprender a partir de las predicciones de los clasificadores base, mejorando la precisión.
Modelo final y serialización:
- Al finalizar todas las etapas, se identifica el modelo con el mejor rendimiento global.
- Este modelo final se serializa en un archivo
.pklpara su implementación en entornos de producción.
Este enfoque iterativo y estructurado garantiza que el modelo final no solo sea preciso, sino que también generalice bien en la detección de fraudes.
⚙️ Configuración del Ambiente e Importación de Datos¶
En esta sección, se importan las bibliotecas necesarias para trabajar en el proyecto. Configuramos el entorno para visualizar todas las columnas de la tabla sin que la información se corte. También habilitamos el acceso a Google Drive para la lectura del archivo CSV y, finalmente, importamos y organizamos el conjunto de datos.
✔️Instalar las bibliotecas necesarias¶
In [3]:
%%time
import sys
import subprocess
from importlib.metadata import version, PackageNotFoundError
# Lista de librerías requeridas
required_libraries = [
'xgboost', 'catboost', 'lightgbm',
'imbalanced-learn', 'scikit-learn', 'nbconvert',
'notebook-as-pdf', 'PyPDF2==2.12.1', 'pyppeteer',
'adjustText', 'mplcursors', 'joblib', 'optuna',
'weasyprint', 'dask-expr', 'shap', 'nbconvert[webpdf] pyppeteer',
'playwright'
]
# Lista de dependencias del sistema necesarias para Pandoc y exportación a PDF
system_dependencies = [
'texlive-xetex', 'texlive-fonts-recommended', 'pandoc'
]
# Función para instalar dependencias del sistema en Google Colab
def install_system_dependencies(dependencies):
try:
print(("Instalando dependencias del sistema necesarias para "
"Pandoc y nbconvert..."))
subprocess.check_call(['apt-get', 'update'], stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
subprocess.check_call(['apt-get', 'install', '-y'] + dependencies,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
print("Instalación de dependencias del sistema completada.")
except subprocess.CalledProcessError as e:
print(f"Error instalando dependencias del sistema: {e}")
sys.exit("No se pudieron instalar las dependencias del sistema. "\
"Verifique los permisos o la configuración de su entorno.")
# Función para instalar librerías de Python faltantes
def install_libraries(libraries):
for lib in libraries:
try:
print(f"Instalando {lib}...")
subprocess.check_call([sys.executable, '-m', 'pip', 'install', lib])
except Exception as e:
print(f"Error instalando {lib}: {e}")
# Verificar qué librerías están faltando
missing_libraries = []
for lib in required_libraries:
try:
# Verificar si ya está instalada
version(lib.split('==')[0]) # Separar si hay versión específica
print(f"{lib} ya está instalado, versión: \
{version(lib.split('==')[0])}")
except PackageNotFoundError:
print(f"{lib} no está instalado.")
missing_libraries.append(lib)
# Resolver conflictos de versiones para urllib3 si pyppeteer está en la lista
if 'pyppeteer' in missing_libraries or 'pyppeteer' in required_libraries:
try:
print("Resolviendo conflictos con urllib3 para pyppeteer...")
subprocess.check_call([sys.executable, '-m', 'pip', 'uninstall',
'urllib3', '-y'])
subprocess.check_call([sys.executable, '-m', 'pip', 'install',
'urllib3<2.0.0'])
print("Versión de urllib3 ajustada para compatibilidad con pyppeteer.")
except Exception as e:
print(f"Error ajustando urllib3: {e}")
# Instalar dependencias del sistema en Google Colab
install_system_dependencies(system_dependencies)
# Instalar librerías faltantes
if missing_libraries:
install_libraries(missing_libraries)
else:
print("Todas las librerías requeridas ya están instaladas.")
# Imprimir las versiones de las librerías instaladas
print("\nVersiones de las librerías instaladas:")
for lib in required_libraries:
try:
print(f"{lib}: {version(lib.split('==')[0])}")
except PackageNotFoundError:
print(f"{lib}: No instalado")
# Verificar todas las librerías instaladas
#print("\nListado completo de librerías instaladas:")
#!pip list
xgboost ya está instalado, versión: 2.1.3 catboost no está instalado. lightgbm ya está instalado, versión: 4.5.0 imbalanced-learn ya está instalado, versión: 0.12.4 scikit-learn ya está instalado, versión: 1.6.0 nbconvert ya está instalado, versión: 7.16.4 notebook-as-pdf no está instalado. PyPDF2==2.12.1 no está instalado. pyppeteer no está instalado. adjustText no está instalado. mplcursors no está instalado. joblib ya está instalado, versión: 1.4.2 optuna no está instalado. weasyprint no está instalado. dask-expr no está instalado. shap ya está instalado, versión: 0.46.0 nbconvert[webpdf] pyppeteer no está instalado. playwright ya está instalado, versión: 1.49.1 Resolviendo conflictos con urllib3 para pyppeteer... Versión de urllib3 ajustada para compatibilidad con pyppeteer. Instalando dependencias del sistema necesarias para Pandoc y nbconvert... Instalación de dependencias del sistema completada. Instalando catboost... Instalando notebook-as-pdf... Instalando PyPDF2==2.12.1... Instalando pyppeteer... Instalando adjustText... Instalando mplcursors... Instalando optuna... Instalando weasyprint... Instalando dask-expr... Instalando nbconvert[webpdf] pyppeteer... Error instalando nbconvert[webpdf] pyppeteer: Command '['/usr/bin/python3', '-m', 'pip', 'install', 'nbconvert[webpdf] pyppeteer']' returned non-zero exit status 1. Versiones de las librerías instaladas: xgboost: 2.1.3 catboost: 1.2.7 lightgbm: 4.5.0 imbalanced-learn: 0.12.4 scikit-learn: 1.6.0 nbconvert: 7.16.4 notebook-as-pdf: 0.5.0 PyPDF2==2.12.1: 2.12.1 pyppeteer: 2.0.0 adjustText: 1.3.0 mplcursors: 0.6 joblib: 1.4.2 optuna: 4.1.0 weasyprint: 63.1 dask-expr: 1.1.21 shap: 0.46.0 nbconvert[webpdf] pyppeteer: No instalado playwright: 1.49.1 CPU times: user 764 ms, sys: 108 ms, total: 872 ms Wall time: 2min 21s
✔️Importar las bibliotecas necesarias¶
In [4]:
# Librerías básicas: Manejo de datos, visualización.
import pandas as pd # Manejo y manipulación de datos tabulares
import numpy as np # Operaciones numéricas, matrices y arreglos
import matplotlib.pyplot as plt # Creación de gráficos y visualizaciones
import seaborn as sns # Visualización de datos estadísticos
from adjustText import adjust_text # Evitar solapamiento en gráficos
import mplcursors # Interacción con gráficos (hover)
import os
# Procesamiento de datos: Preprocesamiento y reducción de dimensiones.
from sklearn.preprocessing import RobustScaler # Escalado robusto para datos con outliers
from sklearn.decomposition import PCA # Reducción de dimensionalidad
from sklearn.feature_selection import mutual_info_classif # Selección de características basada en información mutua
# Validación y división de datos: División en train/test y validación cruzada.
from sklearn.model_selection import (
train_test_split, # División en conjuntos de entrenamiento y prueba
StratifiedKFold, # Validación cruzada estratificada
cross_val_score, # Validación cruzada rápida
GridSearchCV # Búsqueda de hiperparámetros mediante cuadrícula
)
# Desbalance de clases: Técnicas de SMOTE, undersampling y combinaciones.
from imblearn.over_sampling import SMOTE # Oversampling de clases minoritarias
from imblearn.combine import SMOTEENN # Combinación de SMOTE y undersampling
from imblearn.under_sampling import RandomUnderSampler # Undersampling de clases mayoritarias
from imblearn.pipeline import Pipeline
# Algoritmos: Modelos de Machine Learning.
from sklearn.ensemble import (
RandomForestClassifier, # Bosques aleatorios
IsolationForest, # Detección de anomalías
VotingClassifier, # Ensemble de votación
StackingClassifier # Ensemble de apilamiento
)
from sklearn.linear_model import LogisticRegression # Regresión logística
from catboost import CatBoostClassifier # CatBoost para clasificación
from xgboost import XGBClassifier # XGBoost para clasificación
from lightgbm import LGBMClassifier # LightGBM para clasificación
from sklearn.model_selection import StratifiedKFold
# Métricas de evaluación: Métricas específicas utilizadas en el proyecto.
from sklearn.metrics import (
accuracy_score, # Exactitud del modelo
precision_score, # Precisión
recall_score, # Sensibilidad o Recall
f1_score, # F1-Score: equilibrio entre precisión y recall
roc_auc_score, # Área bajo la curva ROC
average_precision_score, # Área bajo la curva Precision-Recall
confusion_matrix, # Matriz de confusión
ConfusionMatrixDisplay, # Visualización de la matriz de confusión
precision_recall_curve, # Curva Precision-Recall
roc_curve, # Curva ROC
auc, # Cálculo del área bajo la curva
classification_report, # Resumen completo de métricas
balanced_accuracy_score, # Precisión balanceada
matthews_corrcoef # Correlación de Matthews
)
# Optimización de hiperparámetros: Herramientas para afinar modelos.
import optuna # Optimización bayesiana de hiperparámetros
from sklearn.model_selection import GridSearchCV # Búsqueda exhaustiva de hiperparámetros
import joblib # Serialización y deserialización de modelos entrenados
import shap
# Librerías adicionales: Estadísticas, selección de características, medición de tiempo.
from scipy.stats import shapiro # Prueba de normalidad
import time # Evaluar tiempo de procesamiento
# Estilización: Herramientas para mejorar la visualización en consola.
from termcolor import colored as style # Colorear texto en la salida de la consola
import dask
✔️Configurar el entorno¶
In [5]:
import warnings
warnings.filterwarnings("ignore")
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.8f}'.format
#pd.set_option('display.width', 1000)
# Ajustar el ancho del contenedor de Jupyter/Colab:
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
plt.style.use('ggplot')
#plt.style.use('fivethirtyeight')
#plt.rcParams['figure.figsize'] = (10, 6)
✔️Configurar Drive¶
In [6]:
%%time
# Montar el drive para obtener el csv
from google.colab import drive
# Monta Google Drive
drive.mount('/content/drive')
# Acceder al archivo CSV proporcionando la ruta correcta desde tu Google Drive
file_path = '/content/drive/MyDrive/Curso_IA_SkillUp_IBM/creditcard.csv'
Mounted at /content/drive CPU times: user 1.25 s, sys: 205 ms, total: 1.45 s Wall time: 22.3 s
✔️Importar y organiza el dataset¶
In [ ]:
# Organizar los datos en un dataframe
data = pd.read_csv(file_path)
In [ ]:
print("Numero de Filas:", data.shape[0])
print("Numero de Columnas", data.shape[1])
Numero de Filas: 284807 Numero de Columnas 31
In [ ]:
# Identificando los nombre de todas las columnas
data.columns
Out[ ]:
Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',
'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',
'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',
'Class'],
dtype='object')
In [ ]:
# Información de los atributos
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 284807 entries, 0 to 284806 Data columns (total 31 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Time 284807 non-null float64 1 V1 284807 non-null float64 2 V2 284807 non-null float64 3 V3 284807 non-null float64 4 V4 284807 non-null float64 5 V5 284807 non-null float64 6 V6 284807 non-null float64 7 V7 284807 non-null float64 8 V8 284807 non-null float64 9 V9 284807 non-null float64 10 V10 284807 non-null float64 11 V11 284807 non-null float64 12 V12 284807 non-null float64 13 V13 284807 non-null float64 14 V14 284807 non-null float64 15 V15 284807 non-null float64 16 V16 284807 non-null float64 17 V17 284807 non-null float64 18 V18 284807 non-null float64 19 V19 284807 non-null float64 20 V20 284807 non-null float64 21 V21 284807 non-null float64 22 V22 284807 non-null float64 23 V23 284807 non-null float64 24 V24 284807 non-null float64 25 V25 284807 non-null float64 26 V26 284807 non-null float64 27 V27 284807 non-null float64 28 V28 284807 non-null float64 29 Amount 284807 non-null float64 30 Class 284807 non-null int64 dtypes: float64(30), int64(1) memory usage: 67.4 MB
In [ ]:
# Muestra las primeras 5 filas del dataframe
print("Muestra las primeras 5 filas del dataframe:")
print(data.head())
Muestra las primeras 5 filas del dataframe:
Time V1 V2 V3 V4 V5 \
0 0.00000000 -1.35980713 -0.07278117 2.53634674 1.37815522 -0.33832077
1 0.00000000 1.19185711 0.26615071 0.16648011 0.44815408 0.06001765
2 1.00000000 -1.35835406 -1.34016307 1.77320934 0.37977959 -0.50319813
3 1.00000000 -0.96627171 -0.18522601 1.79299334 -0.86329128 -0.01030888
4 2.00000000 -1.15823309 0.87773675 1.54871785 0.40303393 -0.40719338
V6 V7 V8 V9 V10 V11 \
0 0.46238778 0.23959855 0.09869790 0.36378697 0.09079417 -0.55159953
1 -0.08236081 -0.07880298 0.08510165 -0.25542513 -0.16697441 1.61272666
2 1.80049938 0.79146096 0.24767579 -1.51465432 0.20764287 0.62450146
3 1.24720317 0.23760894 0.37743587 -1.38702406 -0.05495192 -0.22648726
4 0.09592146 0.59294075 -0.27053268 0.81773931 0.75307443 -0.82284288
V12 V13 V14 V15 V16 V17 \
0 -0.61780086 -0.99138985 -0.31116935 1.46817697 -0.47040053 0.20797124
1 1.06523531 0.48909502 -0.14377230 0.63555809 0.46391704 -0.11480466
2 0.06608369 0.71729273 -0.16594592 2.34586495 -2.89008319 1.10996938
3 0.17822823 0.50775687 -0.28792375 -0.63141812 -1.05964725 -0.68409279
4 0.53819555 1.34585159 -1.11966983 0.17512113 -0.45144918 -0.23703324
V18 V19 V20 V21 V22 V23 \
0 0.02579058 0.40399296 0.25141210 -0.01830678 0.27783758 -0.11047391
1 -0.18336127 -0.14578304 -0.06908314 -0.22577525 -0.63867195 0.10128802
2 -0.12135931 -2.26185710 0.52497973 0.24799815 0.77167940 0.90941226
3 1.96577500 -1.23262197 -0.20803778 -0.10830045 0.00527360 -0.19032052
4 -0.03819479 0.80348692 0.40854236 -0.00943070 0.79827849 -0.13745808
V24 V25 V26 V27 V28 Amount \
0 0.06692807 0.12853936 -0.18911484 0.13355838 -0.02105305 149.62000000
1 -0.33984648 0.16717040 0.12589453 -0.00898310 0.01472417 2.69000000
2 -0.68928096 -0.32764183 -0.13909657 -0.05535279 -0.05975184 378.66000000
3 -1.17557533 0.64737603 -0.22192884 0.06272285 0.06145763 123.50000000
4 0.14126698 -0.20600959 0.50229222 0.21942223 0.21515315 69.99000000
Class
0 0
1 0
2 0
3 0
4 0
In [ ]:
# Muestra las ultimas 5 filas del dataframe
print("Muestra las ultimas 5 filas del dataframe:")
print(data.tail())
Muestra las ultimas 5 filas del dataframe:
Time V1 V2 V3 V4 \
284802 172786.00000000 -11.88111789 10.07178497 -9.83478346 -2.06665568
284803 172787.00000000 -0.73278867 -0.05508049 2.03502975 -0.73858858
284804 172788.00000000 1.91956501 -0.30125385 -3.24963981 -0.55782812
284805 172788.00000000 -0.24044005 0.53048251 0.70251023 0.68979917
284806 172792.00000000 -0.53341252 -0.18973334 0.70333737 -0.50627124
V5 V6 V7 V8 V9 V10 \
284802 -5.36447278 -2.60683733 -4.91821543 7.30533402 1.91442827 4.35617041
284803 0.86822940 1.05841527 0.02432970 0.29486870 0.58480002 -0.97592606
284804 2.63051512 3.03126010 -0.29682653 0.70841718 0.43245405 -0.48478176
284805 -0.37796113 0.62370772 -0.68617999 0.67914546 0.39208671 -0.39912565
284806 -0.01254568 -0.64961669 1.57700625 -0.41465041 0.48617951 -0.91542665
V11 V12 V13 V14 V15 \
284802 -1.59310526 2.71194079 -0.68925561 4.62694203 -0.92445871
284803 -0.15018885 0.91580191 1.21475585 -0.67514296 1.16493091
284804 0.41161374 0.06311886 -0.18369869 -0.51060184 1.32928351
284805 -1.93384882 -0.96288614 -1.04208166 0.44962444 1.96256312
284806 -1.04045834 -0.03151305 -0.18809290 -0.08431647 0.04133346
V16 V17 V18 V19 V20 V21 \
284802 1.10764060 1.99169111 0.51063233 -0.68291968 1.47582913 0.21345411
284803 -0.71175735 -0.02569286 -1.22117886 -1.54555609 0.05961590 0.21420534
284804 0.14071598 0.31350179 0.39565248 -0.57725184 0.00139597 0.23204504
284805 -0.60857713 0.50992846 1.11398059 2.89784877 0.12743352 0.26524492
284806 -0.30262009 -0.66037665 0.16742993 -0.25611687 0.38294810 0.26105733
V22 V23 V24 V25 V26 V27 \
284802 0.11186374 1.01447990 -0.50934845 1.43680691 0.25003428 0.94365117
284803 0.92438358 0.01246304 -1.01622567 -0.60662399 -0.39525507 0.06847247
284804 0.57822901 -0.03750086 0.64013388 0.26574545 -0.08737060 0.00445477
284805 0.80004874 -0.16329794 0.12320524 -0.56915886 0.54666846 0.10882073
284806 0.64307844 0.37677701 0.00879738 -0.47364870 -0.81826712 -0.00241531
V28 Amount Class
284802 0.82373096 0.77000000 0
284803 -0.05352739 24.79000000 0
284804 -0.02656083 67.88000000 0
284805 0.10453282 10.00000000 0
284806 0.01364891 217.00000000 0
🕵️♀️ Exploración de Datos¶
En esta sección, se realiza un análisis exploratorio de los datos y se contesta algunas preguntas de negocio.
Preguntas de Negocio¶
🤔 Pregunta 1: ¿Cuál es el porcentaje de transacciones fraudulentas y no-fraudulentas en el dataset?
In [ ]:
print('El porcentaje de transacciones no-fraudulentas es de',
round(data['Class'].value_counts()[0]/len(data) * 100,2),
'% en el dataset')
print('El porcentaje de transacciones fraudulentas es de',
round(data['Class'].value_counts()[1]/len(data) * 100,2),
'% en el dataset')
El porcentaje de transacciones no-fraudulentas es de 99.83 % en el dataset El porcentaje de transacciones fraudulentas es de 0.17 % en el dataset
In [ ]:
# Boxplot de Montos de Transacciones por Clase
'''
Esta gráfica complementa bien el análisis y puede servir como entrada para
modelos o reglas de detección de fraudes:
- Permite identificar patrones de comportamiento en los montos de transacciones
fraudulentas.
- Ayuda a entender si los montos fraudulentos son más altos o variables que los
no fraudulentos.
- Proporciona información práctica para definir umbrales de detección de
anomalías.
'''
# Separando los datos para el análisis
fraudulent_data = data[data['Class'] == 1]
non_fraudulent_data = data[data['Class'] == 0]
# Calcular conteos de clases
fraudulent_count = fraudulent_data.shape[0]
non_fraudulent_count = non_fraudulent_data.shape[0]
# Calcular el total de transacciones
total_transactions = fraudulent_count + non_fraudulent_count
# Calcular los porcentajes de transacciones
fraud_percentage = (fraudulent_count / total_transactions) * 100
non_fraud_percentage = (non_fraudulent_count / total_transactions) * 100
# Crear etiquetas con los porcentajes
class_labels = [
f"Fraudulent ({fraud_percentage:.2f}%)",
f"Non-Fraudulent ({non_fraud_percentage:.2f}%)"
]
# ---- Texto explicativo inicial ----
print("📊 **Gráfica: Distribución de Montos de Transacciones por Clase**\n")
print("**Objetivo:**")
print("Este gráfico permite comparar las distribuciones de los montos de transacciones entre clases fraudulentas y no fraudulentas.")
print("\n**¿Qué debemos observar?**")
print("1. La mediana (línea dentro de cada caja) indica el monto típico para cada clase.")
print("2. El tamaño de la caja muestra la dispersión (rango intercuartílico, IQR) de los montos.")
print("3. Los puntos fuera de la caja son **outliers** (valores atípicos), que son especialmente importantes en fraudes.")
print("4. Si la clase fraudulenta tiene montos significativamente más altos o más dispersos, esto puede ser un patrón clave para la detección de fraudes.\n\n")
# Crear Boxplot con colores personalizados
plt.figure(figsize=(10, 6))
palette_colors = {'0': '#3498DB', '1': '#FF5733'} # Colores personalizados para clases
sns.boxplot(x='Class', y='Amount', data=data, palette=palette_colors)
# Configuraciones del gráfico
plt.title("Distribución de Montos de Transacciones por Clase", fontsize=14, fontweight='bold')
plt.xlabel("Clase", fontsize=12)
plt.ylabel("Monto de la Transacción", fontsize=12)
plt.xticks([0, 1], class_labels) # Reemplazar etiquetas con porcentajes
plt.grid(axis='y', linestyle='--', alpha=0.5)
# Mostrar el gráfico
plt.tight_layout()
plt.show()
# ---- Texto explicativo final ----
print("\n🔍 **Interpretación del Gráfico:**")
print("1. Si la caja de las transacciones fraudulentas es más alta o más dispersa, sugiere que los fraudes tienden a tener montos mayores o más variados.")
print("2. Los **outliers** en la clase fraudulenta son especialmente importantes, ya que representan montos atípicos que podrían ser señales claras de fraude.")
print("3. Compara la posición de la mediana entre ambas clases para identificar diferencias significativas en los montos.")
📊 **Gráfica: Distribución de Montos de Transacciones por Clase** **Objetivo:** Este gráfico permite comparar las distribuciones de los montos de transacciones entre clases fraudulentas y no fraudulentas. **¿Qué debemos observar?** 1. La mediana (línea dentro de cada caja) indica el monto típico para cada clase. 2. El tamaño de la caja muestra la dispersión (rango intercuartílico, IQR) de los montos. 3. Los puntos fuera de la caja son **outliers** (valores atípicos), que son especialmente importantes en fraudes. 4. Si la clase fraudulenta tiene montos significativamente más altos o más dispersos, esto puede ser un patrón clave para la detección de fraudes.
🔍 **Interpretación del Gráfico:** 1. Si la caja de las transacciones fraudulentas es más alta o más dispersa, sugiere que los fraudes tienden a tener montos mayores o más variados. 2. Los **outliers** en la clase fraudulenta son especialmente importantes, ya que representan montos atípicos que podrían ser señales claras de fraude. 3. Compara la posición de la mediana entre ambas clases para identificar diferencias significativas en los montos.
👁️ Observación:
- Clases desbalanceadas:
- Se obseva un gran desbalance entre las clases. El porcentaje de transacciones fraudulentas representa apenas un 0.17% de todo el dataset. Mas adelante en el proyecto se analizará técnicas como sobremuestreo o submuestreo.
🤔 Pregunta 2: ¿Cuál es el importe medio de las transacciones fraudulentas y no-fraudulentas?
In [ ]:
# Separando los datos para el análisis
fraudulent_data = data[data['Class'] == 1]
non_fraudulent_data = data[data['Class'] == 0]
# Calcula el importe medio de las transacciones fraudulentas
average_amount_fraudulent = fraudulent_data['Amount'].mean()
# Calcula el importe medio de las transacciones no-fraudulentas
average_amount_non_fraudulent = non_fraudulent_data['Amount'].mean()
print(f"El importe medio de las transacciones fraudulentas es: ",
round(average_amount_fraudulent,2))
print(f"El importe medio de las transacciones no-fraudulentas (legítimas) es: ",
round(average_amount_non_fraudulent,2))
El importe medio de las transacciones fraudulentas es: 122.21 El importe medio de las transacciones no-fraudulentas (legítimas) es: 88.29
👁️ Observación:
- Importe medio de las transaciones:
Se observa que el importe medio de las transacciones fraudulentas es significativamente mayor en comparación con las transacciones no fraudulentas, lo cual puede servir como referencia para establecer umbrales en la detección de anomalías.
Análisis de los datos¶
Resumen de las características del dataset.
In [ ]:
# Montos que mas se repiten en las transacciones fraudulentas
#fraudulent_data['Amount'].value_counts()
# Montos (>1) que más se repiten en las transacciones fraudulentas (2 decimales y
#ordenados)
fraudulent_data['Amount'] = fraudulent_data['Amount'].round(2) # Redondear a 2 decimales
amount_counts = (
fraudulent_data['Amount']
.value_counts()
.sort_values(ascending=False)
) # Contar y ordenar
# Filtrar solo montos que se repiten más de una vez
repeated_amounts = amount_counts[amount_counts > 1].sort_values(ascending=False)
# Mostrar los resultados con formato de dos decimales
print("Montos (>1) que más se repiten en las transacciones fraudulentas:")
for amount, count in repeated_amounts.items():
print(f"{amount:.2f}: {count}")
Montos (>1) que más se repiten en las transacciones fraudulentas: 1.00: 113 0.00: 27 99.99: 27 0.76: 17 0.77: 10 0.01: 5 2.00: 4 3.79: 4 1.10: 3 12.31: 3 2.28: 3 1.18: 3 0.68: 3 39.45: 2 30.31: 2 44.90: 2 94.82: 2 1.59: 2 1.63: 2 105.89: 2 101.50: 2 88.23: 2 45.51: 2 104.03: 2 19.02: 2 1.52: 2 78.00: 2 316.06: 2 7.59: 2 8.00: 2 18.96: 2 512.25: 2 0.83: 2 252.92: 2 723.21: 2 188.52: 2 111.70: 2
In [ ]:
# Conteo de transacciones con Amount=0 por clase
amount_zero_fraud = fraudulent_data[fraudulent_data['Amount'] == 0].shape[0]
amount_zero_nonfraud = data[(data['Class'] == 0) & \
(data['Amount'] == 0)].shape[0]
# Porcentajes relativos
print("Transacciones fraudulentas con Amount=0:", amount_zero_fraud)
print("Transacciones no fraudulentas con Amount=0:", amount_zero_nonfraud)
print("Porcentaje en fraudulentas:", \
(amount_zero_fraud / len(fraudulent_data)) * 100, "%")
print("Porcentaje en no fraudulentas:", \
(amount_zero_nonfraud / len(data[data['Class'] == 0])) * 100, "%")
Transacciones fraudulentas con Amount=0: 27 Transacciones no fraudulentas con Amount=0: 1798 Porcentaje en fraudulentas: 5.487804878048781 % Porcentaje en no fraudulentas: 0.6323971651161564 %
👁️ Observación:
- Mantener las transacciones con Amount = 0:
Aunque hay menos transacciones fraudulentas con Amount = 0 en términos absolutos, la proporción más alta dentro de las transacciones fraudulentas hace que sea relevante mantener estos datos en el análisis.
Podrían representar un patrón importante, como intentos de fraude con transacciones pequeñas o pruebas de tarjeta.
In [ ]:
# Verificando el estadístico de la variable monto para transacciones
# fraudulenas
fraudulent_data[['Amount']].describe()
Out[ ]:
| Amount | |
|---|---|
| count | 492.00000000 |
| mean | 122.21132114 |
| std | 256.68328830 |
| min | 0.00000000 |
| 25% | 1.00000000 |
| 50% | 9.25000000 |
| 75% | 105.89000000 |
| max | 2125.87000000 |
In [ ]:
# Verificando el estadístico de la variable monto para transacciones legítimas
# (No-Fraudulentas)
non_fraudulent_data[['Amount']].describe()
Out[ ]:
| Amount | |
|---|---|
| count | 284315.00000000 |
| mean | 88.29102242 |
| std | 250.10509223 |
| min | 0.00000000 |
| 25% | 5.65000000 |
| 50% | 22.00000000 |
| 75% | 77.05000000 |
| max | 25691.16000000 |
In [ ]:
# Estadísticas descriptivas del dataset
print(data.describe().T)
count mean std min \
Time 284807.00000000 94813.85957508 47488.14595457 0.00000000
V1 284807.00000000 0.00000000 1.95869580 -56.40750963
V2 284807.00000000 0.00000000 1.65130858 -72.71572756
V3 284807.00000000 -0.00000000 1.51625501 -48.32558936
V4 284807.00000000 0.00000000 1.41586857 -5.68317120
V5 284807.00000000 0.00000000 1.38024673 -113.74330671
V6 284807.00000000 0.00000000 1.33227109 -26.16050594
V7 284807.00000000 -0.00000000 1.23709360 -43.55724157
V8 284807.00000000 0.00000000 1.19435290 -73.21671846
V9 284807.00000000 -0.00000000 1.09863209 -13.43406632
V10 284807.00000000 0.00000000 1.08884977 -24.58826244
V11 284807.00000000 0.00000000 1.02071303 -4.79747346
V12 284807.00000000 -0.00000000 0.99920139 -18.68371463
V13 284807.00000000 0.00000000 0.99527423 -5.79188121
V14 284807.00000000 0.00000000 0.95859561 -19.21432549
V15 284807.00000000 0.00000000 0.91531601 -4.49894468
V16 284807.00000000 0.00000000 0.87625289 -14.12985452
V17 284807.00000000 -0.00000000 0.84933706 -25.16279937
V18 284807.00000000 0.00000000 0.83817621 -9.49874592
V19 284807.00000000 0.00000000 0.81404050 -7.21352743
V20 284807.00000000 0.00000000 0.77092502 -54.49772049
V21 284807.00000000 0.00000000 0.73452401 -34.83038214
V22 284807.00000000 -0.00000000 0.72570156 -10.93314370
V23 284807.00000000 0.00000000 0.62446030 -44.80773520
V24 284807.00000000 0.00000000 0.60564707 -2.83662692
V25 284807.00000000 0.00000000 0.52127807 -10.29539707
V26 284807.00000000 0.00000000 0.48222701 -2.60455055
V27 284807.00000000 -0.00000000 0.40363249 -22.56567932
V28 284807.00000000 -0.00000000 0.33008326 -15.43008391
Amount 284807.00000000 88.34961925 250.12010924 0.00000000
Class 284807.00000000 0.00172749 0.04152719 0.00000000
25% 50% 75% max
Time 54201.50000000 84692.00000000 139320.50000000 172792.00000000
V1 -0.92037338 0.01810880 1.31564169 2.45492999
V2 -0.59854991 0.06548556 0.80372387 22.05772899
V3 -0.89036484 0.17984634 1.02719554 9.38255843
V4 -0.84864012 -0.01984653 0.74334129 16.87534403
V5 -0.69159707 -0.05433583 0.61192644 34.80166588
V6 -0.76829561 -0.27418708 0.39856490 73.30162555
V7 -0.55407588 0.04010308 0.57043607 120.58949395
V8 -0.20862974 0.02235804 0.32734586 20.00720837
V9 -0.64309757 -0.05142873 0.59713903 15.59499461
V10 -0.53542573 -0.09291738 0.45392345 23.74513612
V11 -0.76249420 -0.03275735 0.73959341 12.01891318
V12 -0.40557149 0.14003259 0.61823803 7.84839208
V13 -0.64853930 -0.01356806 0.66250496 7.12688296
V14 -0.42557401 0.05060132 0.49314985 10.52676605
V15 -0.58288428 0.04807155 0.64882081 8.87774160
V16 -0.46803677 0.06641332 0.52329631 17.31511152
V17 -0.48374831 -0.06567575 0.39967498 9.25352625
V18 -0.49884980 -0.00363631 0.50080675 5.04106919
V19 -0.45629892 0.00373482 0.45894936 5.59197143
V20 -0.21172136 -0.06248109 0.13304084 39.42090425
V21 -0.22839495 -0.02945017 0.18637720 27.20283916
V22 -0.54235037 0.00678194 0.52855364 10.50309009
V23 -0.16184635 -0.01119293 0.14764206 22.52841169
V24 -0.35458614 0.04097606 0.43952660 4.58454914
V25 -0.31714505 0.01659350 0.35071556 7.51958868
V26 -0.32698393 -0.05213911 0.24095217 3.51734561
V27 -0.07083953 0.00134215 0.09104512 31.61219811
V28 -0.05295979 0.01124383 0.07827995 33.84780782
Amount 5.60000000 22.00000000 77.16500000 25691.16000000
Class 0.00000000 0.00000000 0.00000000 1.00000000
In [ ]:
# Calcular la matriz de correlación
correlation_matrix = data.corr()
# Oculta los valores redundantes de la matriz de correlación, mostrando solo la
# mitad inferior.
#mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))
# Visualizar la matriz de correlación
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix,
#mask=mask,
annot=False,
cmap='coolwarm',
linewidths=0.5,
fmt='.2f')
plt.title("Matriz de Correlación para Datos Desbalanceados")
plt.show()
Matriz de Correlación - Clase Fraudulenta (Class = 1)
- Analizamos si hay correlación entre las variables solamente para las clases fraudulentas.
In [ ]:
# ================================
# Filtrar solo los registros de fraude (Class = 1)
# ================================
fraudulent_data = data[data['Class'] == 1]
# ================================
# Calcular la matriz de correlación solo para registros de fraude
# ================================
correlation_matrix = fraudulent_data.corr()
# ================================
# Crear un heatmap con valores legibles
# ================================
plt.figure(figsize=(14, 12)) # Ajustar tamaño del gráfico
sns.heatmap(
correlation_matrix, # Matriz de correlación filtrada
annot=True, # Mostrar valores numéricos en las celdas
fmt=".2f", # Formato para mostrar solo 2 decimales
#cmap='YlGnBu', # Paleta de colores amarillo-verde-azul
cmap='coolwarm', # Paleta de colores azul-rojo
cbar=True, # Mostrar la barra de colores
linewidths=0.5, # Separación entre celdas
annot_kws={"size": 8} # Reducir tamaño del texto de anotaciones
)
# ================================
# Personalización del gráfico
# ================================
plt.title("Correlación de Variables (Solo Registros de Fraude: Class = 1)", fontsize=14)
#plt.xticks(rotation=45) # Rotar etiquetas del eje X para mejor visibilidad
plt.xticks(rotation=90, ha='center') # Rotar etiquetas del eje X a 90°
plt.yticks(rotation=0) # Etiquetas del eje Y horizontales
plt.show()
Identificar pares de variables con alta correlación (umbral > 0.7 o < -0.7)
In [ ]:
# Filtrar pares con alta correlación (umbral > 0.7 o < -0.7)
correlated_pairs = correlation_matrix.abs().unstack().sort_values(ascending=False)
high_correlation_pairs = correlated_pairs[(correlated_pairs > 0.7) & (correlated_pairs < 1.0)]
print(high_correlation_pairs)
V17 V18 0.97149216
V18 V17 0.97149216
V17 V16 0.96015330
V16 V17 0.96015330
V18 V16 0.94449768
V16 V18 0.94449768
V1 V3 0.90787501
V3 V1 0.90787501
V1 V7 0.89760878
V7 V1 0.89760878
V1 V5 0.89496833
V5 V1 0.89496833
V12 V11 0.88971960
V11 V12 0.88971960
V3 V5 0.88368938
V5 V3 0.88368938
V3 V7 0.88231242
V7 V3 0.88231242
V12 V16 0.88170346
V16 V12 0.88170346
V3 V2 0.87690369
V2 V3 0.87690369
V9 V10 0.86396596
V10 V9 0.86396596
V7 V2 0.86298308
V2 V7 0.86298308
V11 V14 0.86204427
V14 V11 0.86204427
V10 V7 0.85982293
V7 V10 0.85982293
V21 V22 0.85211151
V22 V21 0.85211151
V12 V17 0.84279621
V17 V12 0.84279621
V7 V5 0.83973777
V5 V7 0.83973777
V10 V12 0.83433069
V12 V10 0.83433069
V2 V5 0.82839063
V5 V2 0.82839063
V2 V1 0.81922580
V1 V2 0.81922580
V4 V9 0.81885318
V9 V4 0.81885318
V10 V3 0.81824320
V3 V10 0.81824320
V16 V10 0.80663705
V10 V16 0.80663705
V17 0.80310922
V17 V10 0.80310922
V12 V14 0.79982192
V14 V12 0.79982192
V18 V12 0.79375348
V12 V18 0.79375348
V18 V10 0.78650801
V10 V18 0.78650801
V4 V12 0.77876419
V12 V4 0.77876419
V5 V10 0.76458898
V10 V5 0.76458898
V9 V7 0.75472517
V7 V9 0.75472517
V11 V16 0.75449174
V16 V11 0.75449174
V18 V5 0.74559856
V5 V18 0.74559856
V8 V6 0.74302050
V6 V8 0.74302050
V2 V10 0.74108525
V10 V2 0.74108525
V3 V9 0.73320790
V9 V3 0.73320790
V7 V18 0.73140577
V18 V7 0.73140577
V10 V4 0.72697113
V4 V10 0.72697113
V5 V17 0.72379883
V17 V5 0.72379883
V3 V4 0.72376562
V4 V3 0.72376562
V11 V4 0.72168188
V4 V11 0.72168188
V10 V11 0.71750749
V11 V10 0.71750749
V17 V9 0.71388678
V9 V17 0.71388678
V12 V9 0.71058370
V9 V12 0.71058370
V10 V1 0.71011195
V1 V10 0.71011195
V7 V17 0.70308740
V17 V7 0.70308740
V11 0.70265071
V11 V17 0.70265071
dtype: float64
👁️ Observación:
Filtrar pares con alta correlación (umbral > 0.7 o < -0.7):
El umbral de correlación > 0.7 o < -0.7 es un valor comúnmente utilizado en análisis estadístico y machine learning porque:
Correlación alta
- Un valor mayor que 0.7 o menor que -0.7 indica una relación lineal fuerte entre las variables.
- Esto puede implicar redundancia o multicolinealidad en un modelo.
- Las relaciones lineales fuertes pueden afectar negativamente el rendimiento de ciertos modelos, como:
- Regresión lineal
- Modelos basados en distancia (ej., KNN).
VIF (Variance Inflation Factor)
VIF mide la multicolinealidad entre las características. Si dos variables están altamente correlacionadas, una de ellas puede ser eliminada.
- VIF > 10: Alta multicolinealidad → Eliminar o combinar la variable.
- VIF entre 5-10: Requiere análisis adicional.
- VIF < 5: Multicolinealidad aceptable.
In [ ]:
from statsmodels.stats.outliers_influence import variance_inflation_factor
# Seleccionar las columnas no transformadas
X_vif = fraudulent_data[['Time', 'Amount']]
# Calcular VIF para cada característica
vif = pd.DataFrame()
vif['Feature'] = X_vif.columns
vif['VIF'] = [variance_inflation_factor(X_vif.values, i) for i in range(X_vif.shape[1])]
print("VIF por característica:")
print(vif)
VIF por característica: Feature VIF 0 Time 1.18228779 1 Amount 1.18228779
Importancia de características usando correlación sobre el datase "data"
Verificamos si hay correlación absoluta con la variable objetivo antes de realizar la limpieza de los datos.
In [ ]:
# Correlación: Cuando tienes datos numéricos y buscas una relación lineal.
%%time
# Calcular la correlación absoluta con la variable objetivo
correlations = data.drop(columns='Class').corrwith(data['Class']).abs()
# Ordenar características por correlación
correlations = correlations.sort_values(ascending=False)
# Graficar la importancia de las características
plt.figure(figsize=(10, 6))
correlations.plot(kind='bar', color='skyblue')
plt.title('Importancia de características basada en correlación con la clase')
plt.xlabel('Características')
plt.ylabel('Correlación absoluta')
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()
CPU times: user 952 ms, sys: 71.3 ms, total: 1.02 s Wall time: 2 s
📊 Visualización Inicial de Datos¶
En esta sección hacemos una exploración visual de los datos en busca de patrones y problemas que deben ser corregidos antes de trabajar con los modelos.
Preguntas de Negocio¶
Histogramas y gráficas de correlación relevantes para entender la distribución de los datos.
🤔 Pregunta 1: ¿Cuántas transacciones fraudulentas hay en comparación con las no fraudulentas? (Utiliza un gráfico de barras)
In [ ]:
print('Cantidad Transacciones no-fraudulentas (0):',
non_fraudulent_data.shape[0])
print('Cantidad Transacciones fraudulentas (1):', fraudulent_data.shape[0])
Cantidad Transacciones no-fraudulentas (0): 284315 Cantidad Transacciones fraudulentas (1): 492
In [ ]:
# Muestra la distribución de las transacciones fraudulentas con respecto de las
# no fraudulentas
#colors = ["blue", "red"]
colors = ["#87CEEB", "#FF6347"]
# Configurar la visualización en una fila
fig, axes = plt.subplots(1, figsize=(10, 6))
# Función para agregar etiquetas dentro de las barras
def add_labels(ax):
for p in ax.patches:
ax.annotate(f'{int(p.get_height())}', (p.get_x() + p.get_width() / 2,
p.get_height()),
ha='center', va='bottom', fontsize=12, color='black',
weight='bold')
# Cuenta las veces que ocurre cada clase (0: no fraude, 1: fraude)
ax1 = sns.countplot(x='Class', data=data, palette=colors)
ax1.set_title('Transaciones fraudulentas vs no fraudulentas', fontsize=14,
fontweight='bold')
ax1.set_xlabel("Tipo de Transacción")
ax1.set_ylabel("Cantidad de transacciones")
add_labels(ax1)
plt.xticks([0, 1], ['No-Fraude', 'Fraude'])
plt.show()
🤔 Pregunta 2: ¿Cuál es la distribución de los importes y tiempos de las transacciones Fraudelentas y No-Fraudulentas? ¿Siguen una distribución normal?
In [ ]:
# 1. Histogramas y KDE Plots
# Configurar el tamaño de la figura con subplots en una cuadrícula de 2x2
fig, axs = plt.subplots(2, 2, figsize=(16, 8))
# Histograma y curva de densidad para Amount en transacciones fraudulentas
sns.histplot(fraudulent_data['Amount'], bins=30, edgecolor='black',
color='salmon', kde=True, ax=axs[0, 0])
axs[0, 0].set_xlabel('Importe de la transacción')
axs[0, 0].set_ylabel('Cantidad de Transacciones')
axs[0, 0].set_title('Distribución de los importes de las transacciones \
fraudulentas', fontsize=14, fontweight='bold')
# Histograma y curva de densidad para Amount en transacciones no fraudulentas
sns.histplot(non_fraudulent_data['Amount'], bins=30, edgecolor='black',
color='skyblue', kde=True, ax=axs[0, 1])
axs[0, 1].set_xlabel('Importe de la transacción')
axs[0, 1].set_ylabel('Cantidad de Transacciones')
axs[0, 1].set_title('Distribución de los importes de las transacciones \
no-fraudulentas', fontsize=14, fontweight='bold')
# Evitar notación científica en el eje y
axs[0, 1].ticklabel_format(style='plain', axis='y')
# Histograma y curva de densidad para Time en transacciones fraudulentas
sns.histplot(fraudulent_data['Time'], bins=30, edgecolor='black',
color='lightcoral', kde=True, ax=axs[1, 0])
axs[1, 0].set_xlabel('Tiempo')
axs[1, 0].set_ylabel('Cantidad de Transacciones')
axs[1, 0].set_title('Distribución de los tiempos en las transacciones \
fraudulentas', fontsize=14, fontweight='bold')
# Histograma y curva de densidad para Time en transacciones no fraudulentas
sns.histplot(non_fraudulent_data['Time'], bins=30, edgecolor='black',
color='lightblue', kde=True, ax=axs[1, 1])
axs[1, 1].set_xlabel('Tiempo')
axs[1, 1].set_ylabel('Cantidad de Transacciones')
axs[1, 1].set_title('Distribución de los tiempos en las transacciones \
no-fraudulentas', fontsize=14, fontweight='bold')
# Ajustar la presentación para que las gráficas no se solapen
plt.tight_layout()
plt.show()
👁️ Observación:
Los histogramas presentan la distribución de las variables Amount y Time tanto para transacciones fraudulentas como para no fraudulentas.
Amount:
- Las transacciones fraudulentas y no-fraudulentas suelen tener montos bajos, aunque la cantidad de transacciones no-fraudulentas es significativamente mayor. Esta diferencia en la frecuencia puede ser un factor clave al momento de construir un modelo de detección de fraudes.
Time:
- Los fraudes no parecen concentrarse en horarios específicos, mientras que las transacciones no-fraudulentas muestran patrones temporales más definidos.
Este análisis sugiere que para detectar fraudes, los montos bajos deben ser considerados con atención. Además, la variable Time podría tener un menor impacto en la detección de fraudes, ya que no presenta un patrón claro para distinguir entre transacciones fraudulentas y no fraudulentas.
In [ ]:
# 2. Gráfico Q-Q (Quantile-Quantile Plot)
import scipy.stats as stats
# Configurar la figura con subplots en una cuadrícula de 2x2
fig, axs = plt.subplots(2, 2, figsize=(16, 8))
# Q-Q Plot para Amount en transacciones fraudulentas
stats.probplot(fraudulent_data['Amount'], dist="norm", plot=axs[0, 0])
axs[0, 0].set_title("Q-Q Plot for Amount (Fraudulent)", fontsize=14,
fontweight='bold')
# Q-Q Plot para Amount en transacciones no fraudulentas
stats.probplot(non_fraudulent_data['Amount'], dist="norm", plot=axs[0, 1])
axs[0, 1].set_title("Q-Q Plot for Amount (Non-Fraudulent)", fontsize=14,
fontweight='bold')
# Q-Q Plot para Time en transacciones fraudulentas
stats.probplot(fraudulent_data['Time'], dist="norm", plot=axs[1, 0])
axs[1, 0].set_title("Q-Q Plot for Time (Fraudulent)", fontsize=14,
fontweight='bold')
# Q-Q Plot para Time en transacciones no fraudulentas
stats.probplot(non_fraudulent_data['Time'], dist="norm", plot=axs[1, 1])
axs[1, 1].set_title("Q-Q Plot for Time (Non-Fraudulent)", fontsize=14,
fontweight='bold')
# Ajuste para que las gráficas no se solapen
plt.tight_layout()
plt.show()
👁️ Observación:
Estas gráficas sugieren que tanto Amount como Time no siguen una distribución normal, lo cual es importante al seleccionar métodos de análisis y modelos de machine learning que asuman normalidad..
Amount:
- Amount no sigue una distribución normal en ninguno de los dos casos (fraudulento y no-fraudulento). Esto es esperado en transacciones monetarias, ya que las cantidades suelen estar sesgadas hacia valores bajos, con algunas transacciones en valores altos que representan eventos raros.
Time:
- Time muestra un ajuste más cercano a una distribución normal en el centro, pero se desvía en las colas. Esto puede ser suficiente para ciertos modelos que no requieren una distribución estrictamente normal, pero debes considerar esta información si planeas aplicar técnicas estadísticas que dependen de la normalidad.
In [ ]:
# 3. Pruebas Estadísticas (Shapiro-Wilk o Kolmogorov-Smirnov)
# Nivel de significancia
alpha = 0.05
# Prueba de Shapiro-Wilk para Amount en transacciones fraudulentas
stat, p_value = shapiro(fraudulent_data['Amount'])
print("Amount (Fraudulent) - Estadístico:", stat, ", p-value:", p_value)
if p_value > alpha:
print("Amount (Fraudulent) sigue una distribución normal.")
else:
print("Amount (Fraudulent) no sigue una distribución normal.")
# Prueba de Shapiro-Wilk para Amount en transacciones no fraudulentas
stat, p_value = shapiro(non_fraudulent_data['Amount'])
print("Amount (Non-Fraudulent) - Estadístico:", stat, ", p-value:", p_value)
if p_value > alpha:
print("Amount (Non-Fraudulent) sigue una distribución normal.")
else:
print('Amount (Non-Fraudulent) no sigue una distribución normal. \n')
# Prueba de Shapiro-Wilk para Time en transacciones fraudulentas
stat, p_value = shapiro(fraudulent_data['Time'])
print("Time (Fraudulent) - Estadístico:", stat, ", p-value:", p_value)
if p_value > alpha:
print("Time (Fraudulent) sigue una distribución normal.")
else:
print("Time (Fraudulent) no sigue una distribución normal.")
# Prueba de Shapiro-Wilk para Time en transacciones no fraudulentas
stat, p_value = shapiro(non_fraudulent_data['Time'])
print("Time (Non-Fraudulent) - Estadístico:", stat, ", p-value:", p_value)
if p_value > alpha:
print("Time (Non-Fraudulent) sigue una distribución normal.")
else:
print("Time (Non-Fraudulent) no sigue una distribución normal.")
Amount (Fraudulent) - Estadístico: 0.5253348984091436 , p-value: 2.8247332909327927e-34 Amount (Fraudulent) no sigue una distribución normal. Amount (Non-Fraudulent) - Estadístico: 0.30965871086088315 , p-value: 6.851464669833563e-199 Amount (Non-Fraudulent) no sigue una distribución normal. Time (Fraudulent) - Estadístico: 0.9426319474778967 , p-value: 7.454733029562764e-13 Time (Fraudulent) no sigue una distribución normal. Time (Non-Fraudulent) - Estadístico: 0.9400034429247126 , p-value: 7.379668277117668e-119 Time (Non-Fraudulent) no sigue una distribución normal.
🤔 Pregunta 3: "¿Existen datos atípicos en el dataset? ¿Cómo están distribuidos estos valores?"
In [ ]:
# Visualizar distribuciones de Amount y las variables PCA
%%time
# Configurar la cantidad de columnas en los subplots
columns = data.columns
n_cols = 4 # Número de gráficos por fila
n_rows = -(-len(columns) // n_cols) # Calcular el número de filas necesarias
# Crear la figura y los subplots
fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, n_rows * 4))
axes = axes.flatten() # Aplanar los ejes para iterar fácilmente
# Crear un boxplot para cada columna
for i, col in enumerate(columns):
sns.boxplot(data=data[col], ax=axes[i], color="skyblue")
axes[i].set_title(f"Boxplot de {col}", fontsize=12)
axes[i].set_xlabel("Valores")
axes[i].set_ylabel("")
# Ocultar subplots vacíos si hay más subplots que columnas
for j in range(i + 1, len(axes)):
axes[j].axis('off')
# Ajustar el espacio entre subplots
plt.tight_layout()
plt.show()
CPU times: user 25.9 s, sys: 546 ms, total: 26.4 s Wall time: 29.4 s
👁️ Observación:
Variables (V1 a V28):
- La mayoría tienen valores atípicos (outliers), representados por puntos fuera del rango de los bigotes.
- Las distribuciones tienden a centrarse alrededor de cero, lo cual es común en variables transformadas, como ocurre en datos procesados con PCA.
Time:
- Presenta una distribución más amplia y uniforme sin tantos valores atípicos, indicando que es una variable temporal o secuencial.
Amount:
- Muestra valores atípicos significativos. Las transacciones con montos elevados son claras candidatas a ser anomalías (fraudes), lo cual concuerda con el análisis de fraude.
Class:
- Aquí se confirma la desbalanceada distribución entre clases (
0: no fraude,1: fraude).
La cantidad de transacciones fraudulentas es mínima comparada con las no fraudulentas, lo que afecta al modelo y requiere técnicas para equilibrar las clases.
🤔 Pregunta 4: ¿En qué rangos de importes (Amount) se concentran proporcionalmente más las transacciones fraudulentas en comparación con las no-fraudulentas, y qué implicaciones podría tener esto para mejorar los sistemas de detección de fraude?
Analizar si ciertos rangos de importes tienen un comportamiento inusual en términos de fraude.
Mejorar los sistemas de monitoreo: Ajustar los algoritmos de detección para prestar más atención a esos rangos específicos.
In [ ]:
%%time
# Crear rangos de valores para Amount
bins = [0, 10, 50, 100, 500, 1000, 5000] # Define los rangos
# Etiquetas para los rangos
labels = ['0-10', '10-50', '50-100', '100-500', '500-1000', '1000-5000']
data['Amount_range'] = pd.cut(data['Amount'], bins=bins, labels=labels,
include_lowest=True)
# Contar las transacciones por rango y clase
range_class_counts = data.groupby(['Amount_range',
'Class']).size().unstack(fill_value=0)
# Calcular proporciones relativas por clase
total_counts = data['Class'].value_counts()
range_proportions = range_class_counts.div(total_counts, axis=1)
# Preparar los datos para el gráfico
range_proportions.reset_index(inplace=True)
range_proportions = range_proportions.melt(id_vars='Amount_range',
var_name='Clase',
value_name='Proporción')
# Cambiar las etiquetas de clase a 'No Fraude' y 'Fraude'
range_proportions['Clase'] = range_proportions['Clase'].replace({0: 'No Fraude',
1: 'Fraude'})
# Crear el gráfico de barras dobles
plt.figure(figsize=(10, 6))
ax = sns.barplot(
x='Amount_range',
y='Proporción',
hue='Clase',
data=range_proportions,
#palette=['blue', 'red']
palette=['#87CEEB', '#FF6347']
)
# Añadir etiquetas de valores sobre las barras con formato de porcentaje
for container in ax.containers:
ax.bar_label(
container,
fmt=lambda x: f"{x * 100:.0f}%", # Convertir a porcentaje y redondear
label_type='edge',
color='black',
fontsize=10,
fontweight='bold'
)
# Configurar títulos y etiquetas
plt.title('Proporciones de rangos de importes entre \n transacciones \
Fraudulentas y No-fraudulentas', fontsize=16, fontweight='bold')
plt.xlabel('Rangos de Importe (Amount)', fontsize=14)
plt.ylabel('Proporción', fontsize=14)
plt.xticks(fontsize=12, rotation=45) # Rotar etiquetas del eje X
# Configurar el eje Y con formato de porcentaje
yticks = plt.gca().get_yticks() # Obtener las posiciones de los ticks actuales
plt.gca().set_yticks(yticks) # Establecer las posiciones
plt.gca().set_yticklabels([f"{int(tick * 100)}%" for tick in yticks],
fontsize=12) # Formatear como porcentaje
# Ajustar la leyenda
plt.legend(title='Tipo de Transacción', title_fontsize=14, fontsize=12)
# Mostrar la gráfica
plt.tight_layout()
plt.show()
CPU times: user 584 ms, sys: 117 ms, total: 700 ms Wall time: 715 ms
🤔 Pregunta 5: ¿Los importes (Amount) igual a cero se concentran proporcionalmente más en las transacciones fraudulentas o en las no-fraudulentas?
In [ ]:
# Calculate proportions for transactions with Amount = 0
#proportion_amount_zero = data[data['Amount'] == 0]['Class'].value_counts(normalize=True) * 100
# Filtrar solo las transacciones donde Amount es igual a 0
amount_zero_data = data[data['Amount'] == 0]
# Contar cuántas transacciones con Amount = 0 hay en cada clase
count_amount_zero = amount_zero_data['Class'].value_counts()
# Obtener el total de transacciones en cada clase
total_count_per_class = data['Class'].value_counts()
# Calcular la proporción de Amount = 0 en cada clase
proportion_amount_zero = (count_amount_zero / total_count_per_class).fillna(0)
# Reemplazar etiquetas para mayor claridad
labels = {0: "(0) No-Fraude", 1: "(1) Fraude"}
proportion_amount_zero.index = [labels[i] for i in proportion_amount_zero.index]
# Configuración de estilo con Seaborn
sns.set(style="whitegrid")
# Datos de proporción
labels = ['No-Fraude (0)', 'Fraude (1)']
values = proportion_amount_zero.values
colors = ['#87CEEB', '#FF6347'] # Colores personalizados
# Crear gráfico de tarta (pizza)
fig, ax = plt.subplots(figsize=(8, 6))
wedges, texts, autotexts = ax.pie(
values,
labels=labels,
autopct='%1.1f%%', # Mostrar porcentajes
startangle=140,
colors=colors,
wedgeprops=dict(edgecolor='white'),
explode=(0.05, 0.05) # Separar ligeramente las porciones
)
# Personalizar texto
for text in texts + autotexts:
text.set_fontsize(12)
text.set_color('black')
text.set_fontweight('bold')
# Título del gráfico
plt.title("Proporción de Transacciones con Amount = 0 por Clase", fontsize=14, fontweight='bold')
# Agregar interacción con mplcursors (hover)
cursor = mplcursors.cursor(wedges, hover=True)
@cursor.connect("add")
def on_hover(sel):
sel.annotation.set_text(f"{labels[sel.index]}\n{values[sel.index]:.2f}%")
# Mostrar el gráfico
plt.tight_layout()
plt.show()
👁️ Observación:
- Mantener las transacciones con Amount = 0:
Aunque haya menos transacciones fraudulentas con Amount = 0 en términos absolutos, la proporción más alta dentro de las transacciones fraudulentas hace que sea relevante mantener estos datos en el análisis.
Podrían representar un patrón importante, como intentos de fraude con transacciones pequeñas o pruebas de tarjeta.
🔄 Preprocesamiento¶
Realiza varias operaciones de preprocesamiento y preparación de datos para aplicar modelos de aprendizaje automático.
🧹 Limpieza de datos¶
En esta sección, se realizam el manejo de valores nulos, eliminación de duplicados, escalado de datos numéricos, etc.
Distribución de la variable "Class" antes del proceso de limpieza¶
In [ ]:
# Distribución de transacciones Fraudulentas (0) y
# transacciones fraudulentas(1)
data['Class'].value_counts()
Out[ ]:
| count | |
|---|---|
| Class | |
| 0 | 284315 |
| 1 | 492 |
Valores perdidos¶
In [ ]:
# Verifica la cantidad de valores nulos en cada columna
data.isnull().sum()
Out[ ]:
| 0 | |
|---|---|
| Time | 0 |
| V1 | 0 |
| V2 | 0 |
| V3 | 0 |
| V4 | 0 |
| V5 | 0 |
| V6 | 0 |
| V7 | 0 |
| V8 | 0 |
| V9 | 0 |
| V10 | 0 |
| V11 | 0 |
| V12 | 0 |
| V13 | 0 |
| V14 | 0 |
| V15 | 0 |
| V16 | 0 |
| V17 | 0 |
| V18 | 0 |
| V19 | 0 |
| V20 | 0 |
| V21 | 0 |
| V22 | 0 |
| V23 | 0 |
| V24 | 0 |
| V25 | 0 |
| V26 | 0 |
| V27 | 0 |
| V28 | 0 |
| Amount | 0 |
| Class | 0 |
| Amount_range | 55 |
In [ ]:
# Elimina los valores nulos (sí hubiera)
data.dropna(inplace=True)
Datos duplicados¶
In [ ]:
# Verifica la cantidad de filas duplicadas en el dataframe
duplicados = data.duplicated()
num_duplicados = duplicados.sum()
print(f"Número de filas duplicadas en el dataset: {num_duplicados}")
Número de filas duplicadas en el dataset: 1081
In [ ]:
# Verifica la cantidad de transacciones no-fraudulentas duplicadas en el dataframe
duplicados_nofraude = data[(data.duplicated()) & (data['Class'] == 0)].shape[0]
print(f"Transacciones Legítimas Duplicadas: {duplicados_nofraude}")
Transacciones Legítimas Duplicadas: 1062
In [ ]:
# Verifica la cantidad de transacciones fraudulentas duplicadas en el dataframe
duplicados_fraude = data[(data.duplicated()) & (data['Class'] == 1)].shape[0]
print(f"Transacciones Fraudulents Duplicadas: {duplicados_fraude}")
Transacciones Fraudulents Duplicadas: 19
In [ ]:
# Listar las filas duplicadas considerando todas las columnas
duplicados_completos = data[data.duplicated(keep=False)]
# Mostrar el número de duplicados completos
print(f"Número de transacciones idénticas en todas las columnas: ",
{duplicados_completos.shape[0]})
print("Ejemplos de transacciones duplicadas idénticas:")
# Presenta las 5 primeras observaciones duplicadas
print(duplicados_completos.head())
Número de transacciones idénticas en todas las columnas: {1854}
Ejemplos de transacciones duplicadas idénticas:
Time V1 V2 V3 V4 V5 \
32 26.00000000 -0.52991228 0.87389158 1.34724733 0.14545668 0.41420886
33 26.00000000 -0.52991228 0.87389158 1.34724733 0.14545668 0.41420886
34 26.00000000 -0.53538776 0.86526781 1.35107629 0.14757547 0.43368021
35 26.00000000 -0.53538776 0.86526781 1.35107629 0.14757547 0.43368021
112 74.00000000 1.03837033 0.12748613 0.18445589 1.10994979 0.44169890
V6 V7 V8 V9 V10 V11 \
32 0.10022309 0.71120608 0.17606596 -0.28671693 -0.48468768 0.87248959
33 0.10022309 0.71120608 0.17606596 -0.28671693 -0.48468768 0.87248959
34 0.08698294 0.69303931 0.17974226 -0.28564186 -0.48247447 0.87179958
35 0.08698294 0.69303931 0.17974226 -0.28564186 -0.48247447 0.87179958
112 0.94528253 -0.03671460 0.35099500 0.11894954 -0.24328924 0.57806260
V12 V13 V14 V15 V16 V17 \
32 0.85163586 -0.57174530 0.10097427 -1.51977183 -0.28437598 -0.31052358
33 0.85163586 -0.57174530 0.10097427 -1.51977183 -0.28437598 -0.31052358
34 0.85344743 -0.57182189 0.10225210 -1.51999120 -0.28591250 -0.30963339
35 0.85344743 -0.57182189 0.10225210 -1.51999120 -0.28591250 -0.30963339
112 0.67472982 -0.53423057 0.44660138 1.12288467 -1.76800051 1.24115696
V18 V19 V20 V21 V22 V23 \
32 -0.40424787 -0.82337352 -0.29034761 0.04694907 0.20810486 -0.18554835
33 -0.40424787 -0.82337352 -0.29034761 0.04694907 0.20810486 -0.18554835
34 -0.40390199 -0.82374299 -0.28326378 0.04952569 0.20653654 -0.18710807
35 -0.40390199 -0.82374299 -0.28326378 0.04952569 0.20653654 -0.18710807
112 -2.44949986 -1.74725517 -0.33551985 0.10251980 0.60508853 0.02309216
V24 V25 V26 V27 V28 Amount \
32 0.00103066 0.09881570 -0.55290360 -0.07328808 0.02330705 6.14000000
33 0.00103066 0.09881570 -0.55290360 -0.07328808 0.02330705 6.14000000
34 0.00075301 0.09811661 -0.55347097 -0.07830550 0.02542738 1.77000000
35 0.00075301 0.09811661 -0.55347097 -0.07830550 0.02542738 1.77000000
112 -0.62646266 0.47912027 -0.16693684 0.08124672 0.00119158 1.18000000
Class Amount_range
32 0 0-10
33 0 0-10
34 0 0-10
35 0 0-10
112 0 0-10
In [ ]:
# Eliminar filas duplicadas
data = data.drop_duplicates()
In [ ]:
# Distribución de transacciones no-fraudulentas y transacciones fraudulentas
# después de eliminar duplicados
data['Class'].value_counts()
Out[ ]:
| count | |
|---|---|
| Class | |
| 0 | 283198 |
| 1 | 473 |
In [ ]:
# Numero total de Filas y Columnas después de la eliminación de los datos
# duplicados
data.shape
Out[ ]:
(283671, 32)
Corrección de Sesgos (Skewness) en Características Numéricas¶
En esta sección hacemos tratamiento de los sesgos skewness en todo el dataset "data".
Este proceso identifica columnas numéricas con alta asimetría en sus distribuciones y aplica transformaciones (logaritmo natural o raíz cuadrada) para reducir la skewness. Se comparan visualmente las distribuciones antes y después de la transformación, mostrando una mejora significativa en la simetría de los datos.
Esta corrección mejora el rendimiento y la estabilidad de los modelos de Machine Learning al preparar los datos en un formato más adecuado.
In [ ]:
%%time
from scipy.stats import skew
# ============================================
# Identificar características numéricas
# ============================================
numerical_features = data.select_dtypes(include=['float64', 'int64']).columns
# Excluir la columna 'Class' de las características
numerical_features = numerical_features.drop('Class', errors='ignore')
# ============================================
# Calcular skewness para todo el dataset
# ============================================
skew_values = data[numerical_features].apply(lambda x: skew(x.dropna()))
skew_df = pd.DataFrame({'Column': numerical_features, 'Skewness': skew_values})
skew_df = skew_df.sort_values(by='Skewness', ascending=False)
# Umbral para skewness significativa
threshold = 1
columns_with_high_skew = skew_df[abs(skew_df['Skewness']) > threshold]['Column'].tolist()
print(f"⚠️ Columnas con skewness alta (>|{threshold}|):")
print(columns_with_high_skew)
# Guardar valores originales solo para comparar en las gráficas
original_data = data[columns_with_high_skew].copy()
# ============================================
# Aplicar transformación de skewness en todo el dataset
# ============================================
for col in columns_with_high_skew:
if (data[col] > 0).all(): # Si los valores son positivos
data[col] = np.log1p(data[col])
else: # Si hay valores negativos o mixtos
data[col] = np.sqrt(data[col] - data[col].min() + 1)
# ============================================
# Visualización: Antes y Después de la Transformación
# ============================================
fig, axes = plt.subplots(len(columns_with_high_skew), 2, figsize=(12, 5 * len(columns_with_high_skew)))
fig.suptitle("\nDistribución Antes y Después de la Transformación", fontsize=16)
for i, col in enumerate(columns_with_high_skew):
# Distribución original
axes[i, 0].hist(original_data[col].dropna(), bins=50, color='blue', alpha=0.7)
axes[i, 0].set_title(f'Original: {col}')
# Distribución transformada
axes[i, 1].hist(data[col].dropna(), bins=50, color='green', alpha=0.7)
axes[i, 1].set_title(f'Transformed: {col}')
plt.tight_layout()
plt.subplots_adjust(top=0.95) # Ajustar el título principal
plt.show()
# ============================================
# Comparación de Skewness Antes y Después
# ============================================
skew_values_after = data[columns_with_high_skew].apply(lambda x: skew(x.dropna()))
comparison_df = pd.DataFrame({
'Column': columns_with_high_skew,
'Skewness_Before': skew_df.loc[columns_with_high_skew, 'Skewness'].values,
'Skewness_After': skew_values_after.values
}).sort_values(by='Skewness_After', ascending=False)
print("\n✅ Comparación de Skewness Antes y Después de la Transformación:")
print(comparison_df)
# ============================================
# Validación Final: Distribución de la columna 'Class'
# ============================================
print("\n✅ Distribución de la columna 'Class' después de la transformación:")
print(data['Class'].value_counts())
# Validación Final: Estadísticas de la columna 'Amount'
print("\n✅ Estadísticas de la columna 'Amount' después de la transformación:")
print(data['Amount'].describe())
⚠️ Columnas con skewness alta (>|1|): ['V28', 'Amount', 'V21', 'V10', 'V6', 'V16', 'V7', 'V14', 'V3', 'V12', 'V20', 'V27', 'V1', 'V17', 'V2', 'V23', 'V8']
✅ Comparación de Skewness Antes y Después de la Transformación:
Column Skewness_Before Skewness_After
1 Amount 7.96253473 2.61906002
0 V28 12.23872886 2.41836887
4 V6 1.10472746 0.52916589
3 V10 1.28136542 -0.19545718
2 V21 2.92668084 -1.24625006
5 V16 -1.09115200 -1.94859128
7 V14 -1.92183089 -3.35653552
8 V3 -1.98781931 -3.43521368
9 V12 -2.19822660 -3.56130787
12 V1 -3.17148678 -4.30654934
6 V7 -1.87565556 -4.43710734
11 V27 -3.00247374 -6.14314437
14 V2 -4.02038196 -6.45443785
10 V20 -2.82562203 -7.24348793
13 V17 -3.69088101 -7.74755919
15 V23 -4.79793620 -11.63566776
16 V8 -8.30897873 -13.15432032
✅ Distribución de la columna 'Class' después de la transformación:
Class
0 283198
1 473
Name: count, dtype: int64
✅ Estadísticas de la columna 'Amount' después de la transformación:
count 283671.00000000
mean 6.83089905
std 6.42913925
min 1.00000000
25% 2.56904652
50% 4.79583152
75% 8.85945822
max 70.43330178
Name: Amount, dtype: float64
CPU times: user 13.7 s, sys: 420 ms, total: 14.1 s
Wall time: 14.1 s
Detección de anomalías (método: Isolation Forest)¶
Para este dataset, aplicaremos Isolation Forest ya que es robusto en conjuntos de datos grandes y permite identificar anomalías en variables PCA y escaladas.
El uso del método de detección de anomalías puede beneficiar el proyecto al reducir la influencia de los valores atípicos, lo que potencialmente mejora el rendimiento y la capacidad de generalización del modelo en datos nuevos.
Aplicaremos esta técnica solamente para las transacciones No-Fraudulentas.
In [ ]:
# Aplicar Isolation Forest solo a transacciones no-fraudulentas
# Separar las transacciones no fraudulentas
non_fraud_data = data[data['Class'] == 0]
# Ajusta el parámetro 'contamination' según tus datos
iso = IsolationForest(contamination=0.01, random_state=42)
non_fraud_data['Anomaly'] = iso.fit_predict(non_fraud_data[['Amount']])
# Filtrar transacciones no anómalas
clean_non_fraud_data = non_fraud_data[non_fraud_data['Anomaly'] == 1 \
].drop('Anomaly', axis=1)
# Separar las transacciones fraudulentas (sin cambios)
fraud_data = data[data['Class'] == 1]
# Combinar datos limpios
clean_data = pd.concat([clean_non_fraud_data, fraud_data], axis=0)
# Verificar el resultado
print(f"Transacciones originales: {data.shape[0]}")
print(f"Transacciones después de la limpieza: {clean_data.shape[0]}")
Transacciones originales: 283671 Transacciones después de la limpieza: 280898
Regenerar gráficas con datos limpios¶
Usamos clean_data para generar las gráficas de proporciones y validar que los rangos aún reflejan patrones significativos.
In [ ]:
# Gráfica de proporciones por rangos de Amount:
# Crear el gráfico de barras dobles
plt.figure(figsize=(10, 6))
ax = sns.barplot(
x='Amount_range',
y='Proporción',
hue='Clase',
data=range_proportions,
#palette=['blue', 'red']
palette=['#87CEEB', '#FF6347']
)
# Añadir etiquetas de valores encima de las barras
for container in ax.containers:
# Asegurarnos de mostrar valores pequeños redondeados
for bar, value in zip(container, container.datavalues):
if value > 0: # Evitar mostrar etiquetas para proporciones de 0
ax.text(
bar.get_x() + bar.get_width() / 2, # Posición en X
bar.get_height() + 0.002, # Posición ligeramente encima de la barra
f"{value:.0%}", # Formato de porcentaje con una decimal
ha='center',
va='bottom',
fontsize=12,
color='black',
fontweight='bold'
)
# Configurar títulos y etiquetas
plt.title('Proporciones de rangos de importes entre \n transacciones '
'Fraudulentas y No-fraudulentas (Limpias)',
fontsize=16, fontweight='bold')
plt.xlabel('Rangos de Importe (Amount)', fontsize=14)
plt.ylabel('Proporción (%)', fontsize=14)
plt.xticks(fontsize=12, rotation=45)
# Configurar el eje Y para que muestre valores redondeados
yticks = plt.gca().get_yticks() # Obtener los valores actuales del eje Y
plt.gca().set_yticks(yticks) # Asegurar que los valores permanezcan
plt.gca().set_yticklabels([f"{tick:.0%}" for tick in yticks], fontsize=12) # Formatear como porcentajes
# Ajustar la leyenda
plt.legend(title='Tipo de Transacción', title_fontsize=12, fontsize=12)
# Mostrar la gráfica
plt.tight_layout()
plt.show()
Eliminación de columnas irrelevantes¶
In [ ]:
# Eliminar la columna Time
clean_data = clean_data.drop(['Time', 'Amount_range'], axis=1)
In [ ]:
# Numero de Filas y Columnas después de la eliminación de la columna Time
clean_data.shape
Out[ ]:
(280898, 30)
In [ ]:
# Verificar si la variable Time ha sido eliminada del dataset
print(clean_data.head())
V1 V2 V3 V4 V5 V6 \
0 7.48650135 8.58154685 6.10060085 1.37815522 -0.33832077 5.25574863
1 7.65502232 8.60127190 5.90317407 0.44815408 0.06001765 5.20366651
2 7.48659840 8.50738294 6.03773081 0.37977959 -0.50319813 5.38154302
3 7.51273838 8.57499280 6.03936895 -0.86329128 -0.01030888 5.32988828
4 7.49995177 8.63675080 6.01911138 0.40303393 -0.40719338 5.22076885
V7 V8 V9 V10 V11 V12 \
0 6.69304416 8.62063898 0.36378697 5.06745070 -0.55159953 4.36645323
1 6.66921574 8.61985035 -0.25542513 5.04195280 1.61272666 4.55510153
2 6.73414453 8.62927542 -1.51465432 5.07896695 0.62450146 4.44407452
3 6.69289553 8.63679074 -1.38702406 5.05304963 -0.22648726 4.45667397
4 6.71938854 8.59919681 0.81773931 5.13238121 -0.82284288 4.49687783
V13 V14 V15 V16 V17 V18 \
0 -0.99138985 4.46129534 1.46817697 3.82876664 5.13524786 0.02579058
1 0.48909502 4.48001710 0.63555809 3.94889498 5.10372361 -0.18336127
2 0.71729273 4.47754169 2.34586495 3.49853846 5.22233365 -0.12135931
3 0.50775687 4.46389984 -0.63141812 3.75102750 5.04764367 1.96577500
4 1.34585159 4.36974320 0.17512113 3.83124070 5.09173508 -0.03819479
V19 V20 V21 V22 V23 V24 \
0 0.40399296 5.14526553 5.98431912 0.27783758 6.12825637 0.06692807
1 -0.14578304 5.11402602 5.96695960 -0.63867195 6.14550959 -0.33984648
2 -2.26185710 5.17178160 6.00652814 0.77167940 6.21091075 -0.68928096
3 -1.23262197 5.10042228 5.97679527 0.00527360 6.12173828 -1.17557533
4 0.80348692 5.16051234 5.98506069 0.79827849 6.12605436 0.14126698
V25 V26 V27 V28 Amount Class
0 0.12853936 -0.18911484 4.86818628 3.56228053 12.27273401 0
1 0.16717040 0.12589453 4.85352410 3.56729867 1.92093727 0
2 -0.32764183 -0.13909657 4.84874484 3.55684464 19.48486592 0
3 0.64737603 -0.22192884 4.86090549 3.57384293 11.15795680 0
4 -0.20600959 0.50229222 4.87699719 3.59528146 8.42555636 0
👁️Observación:
- Eliminar la columna Time, ya que no tiene relación significativa con la variable objetivo y no es comparable con las variables transformadas por PCA.
Escalado de Características¶
- Usamos RobustScaler() para escalar Amount. Esto es útil porque RobustScaler utiliza la mediana y el rango intercuartílico (IQR), lo que lo hace menos sensible a los outliers. Esto es especialmente importante para Amount, ya que identificamos la presencia de valores atípicos en esta variable.
In [ ]:
# Escalado de la variable Amount usando RobustScaler
robust_scaler = RobustScaler()
clean_data[['Amount']] = robust_scaler.fit_transform(clean_data[['Amount']])
In [ ]:
# Verificar si la variable Amount ha sido escalada correctamente
print(clean_data.head())
V1 V2 V3 V4 V5 V6 \
0 7.48650135 8.58154685 6.10060085 1.37815522 -0.33832077 5.25574863
1 7.65502232 8.60127190 5.90317407 0.44815408 0.06001765 5.20366651
2 7.48659840 8.50738294 6.03773081 0.37977959 -0.50319813 5.38154302
3 7.51273838 8.57499280 6.03936895 -0.86329128 -0.01030888 5.32988828
4 7.49995177 8.63675080 6.01911138 0.40303393 -0.40719338 5.22076885
V7 V8 V9 V10 V11 V12 \
0 6.69304416 8.62063898 0.36378697 5.06745070 -0.55159953 4.36645323
1 6.66921574 8.61985035 -0.25542513 5.04195280 1.61272666 4.55510153
2 6.73414453 8.62927542 -1.51465432 5.07896695 0.62450146 4.44407452
3 6.69289553 8.63679074 -1.38702406 5.05304963 -0.22648726 4.45667397
4 6.71938854 8.59919681 0.81773931 5.13238121 -0.82284288 4.49687783
V13 V14 V15 V16 V17 V18 \
0 -0.99138985 4.46129534 1.46817697 3.82876664 5.13524786 0.02579058
1 0.48909502 4.48001710 0.63555809 3.94889498 5.10372361 -0.18336127
2 0.71729273 4.47754169 2.34586495 3.49853846 5.22233365 -0.12135931
3 0.50775687 4.46389984 -0.63141812 3.75102750 5.04764367 1.96577500
4 1.34585159 4.36974320 0.17512113 3.83124070 5.09173508 -0.03819479
V19 V20 V21 V22 V23 V24 \
0 0.40399296 5.14526553 5.98431912 0.27783758 6.12825637 0.06692807
1 -0.14578304 5.11402602 5.96695960 -0.63867195 6.14550959 -0.33984648
2 -2.26185710 5.17178160 6.00652814 0.77167940 6.21091075 -0.68928096
3 -1.23262197 5.10042228 5.97679527 0.00527360 6.12173828 -1.17557533
4 0.80348692 5.16051234 5.98506069 0.79827849 6.12605436 0.14126698
V25 V26 V27 V28 Amount Class
0 0.12853936 -0.18911484 4.86818628 3.56228053 1.22386702 0
1 0.16717040 0.12589453 4.85352410 3.56729867 -0.45360592 0
2 -0.32764183 -0.13909657 4.84874484 3.55684464 2.39256810 0
3 0.64737603 -0.22192884 4.86090549 3.57384293 1.04322122 0
4 -0.20600959 0.50229222 4.87699719 3.59528146 0.60044515 0
In [ ]:
# Verificar cantidad de observaciones por clase
clean_data['Class'].value_counts()
Out[ ]:
| count | |
|---|---|
| Class | |
| 0 | 280425 |
| 1 | 473 |
👁️Observación:
- Mantener la columna Amount, ya que representa un dato importante para las transacciones y puede ser útil después de una transformación adecuada.
Evaluación Comparativa de Modelos¶
En esta sección, evaluamos diversas técnicas y algoritmos con el objetivo de analizar cómo un dataset altamente desbalanceado se comporta en distintos escenarios.
Esta exploración nos permitirá identificar el algoritmo más adecuado. Más adelante en el proyecto, una vez seleccionado el mejor algoritmo/modelo, nos enfocaremos en su optimización y ajuste fino para alcanzar las mejores métricas de rendimiento.
Modelos Evaluados:
Se evaluan los modelos tanto en datset original (clean_data) cuanto con el balanceado de clases.
- Regresión Logistica (LogisticRegression)
- RandomForest (RandomForestClassifier)
- XGBoost (XGBClassifier)
- Catboost (CatBoostClassifier)
- LightGBM (LGBMClassifier)
Ensembles:
Se evaluan los ensembles con y sin aplicacion de técnicas de SMOTE.
- StackingClassifier
- VotingClassifier
Balanceado de Clases:
- Oversampling (SMOTE)
- Undersampling (RandomUnderSampler)
Validación Cruzada de los Mejores Modelos:
- Validación Cruzada en dataset original (clean_data)
- Validación Cruzada con SMOTE
Separa el dataset¶
In [ ]:
%%time
# Separar el dataset en características (X) y variable objetivo (y)
# Eliminamos la columna 'Class' de las características
X = clean_data.drop(['Class'], axis=1)
y = clean_data['Class'] # Etiquetamos la variable objetivo
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42, stratify=y)
# Definir algoritmos a probar
models = {
"Logistic Regression": LogisticRegression(max_iter=1000, random_state=42),
"Random Forest": RandomForestClassifier(n_estimators=150, random_state=42),
"XGBoost": XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42),
"CatBoost": CatBoostClassifier(verbose=0, random_state=42),
"LightGBM": LGBMClassifier(force_row_wise=True, random_state=42)
}
print('\nTamaños set de entrenamiento:', X_train.shape, y_train.shape)
# Proporción de clases en el conjunto de entrenamiento
print("\nDistribución en y_train:")
print(y_train.value_counts())
print('\nTamaño set de prueba:', X_test.shape, y_test.shape)
# Proporción de clases en el conjunto de prueba
print("\nDistribución en y_test:")
print(y_test.value_counts())
Tamaños set de entrenamiento: (224718, 29) (224718,) Distribución en y_train: Class 0 224340 1 378 Name: count, dtype: int64 Tamaño set de prueba: (56180, 29) (56180,) Distribución en y_test: Class 0 56085 1 95 Name: count, dtype: int64 CPU times: user 212 ms, sys: 17 ms, total: 229 ms Wall time: 232 ms
Técnicas¶
El uso de técnicas permite:
Comparación Objetiva: Evaluar cómo cada técnica (balanceo, reducción de dimensionalidad, validación cruzada, ensembles) influye en el rendimiento de los algoritmos.
Identificación de la Mejor Configuración: Identificar el mejor enfoque de preprocesamiento y balanceo para optimizar la detección de fraudes.
Flexibilidad: Adaptar el proyecto a diferentes escenarios o datasets, asegurando una solución escalable y reproducible.
In [ ]:
%%time
tecnica_name = "Tecnica 1"
# Evaluar cada modelo y consolidar métricas
results_tecnica_1 = pd.DataFrame() # DataFrame para consolidar resultados globales
for name, model in models.items():
print(f"\n=== Entrenando y evaluando modelo: {name} ===")
# Medir el tiempo de inicio
start_time = time.time()
# Entrenar modelo
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_proba = model.predict_proba(X_test)[:, 1]
# Medir el tiempo de fin
end_time = time.time()
execution_time = end_time - start_time # Tiempo en segundos
# Calcular métricas actualizadas y convertir a porcentaje
auprc = average_precision_score(y_test, y_proba) * 100
f1 = f1_score(y_test, y_pred) * 100
mcc = matthews_corrcoef(y_test, y_pred) * 100
balanced_acc = balanced_accuracy_score(y_test, y_pred) * 100
accuracy = accuracy_score(y_test, y_pred) * 100
precision = precision_score(y_test, y_pred) * 100
recall = recall_score(y_test, y_pred) * 100
# Consolidar métricas globales en un DataFrame
results_tecnica_1 = pd.concat([results_tecnica_1, pd.DataFrame({
"Modelo": [name],
"AUPRC": [auprc],
"Recall": [recall],
"Balanced Accuracy": [balanced_acc],
"F1-Score": [f1],
"MCC": [mcc],
"Accuracy (%)": [accuracy],
"Precision": [precision],
"Execution Time (s)": [execution_time], # Agregar tiempo de ejecución
"Tecnica":[tecnica_name]
})], ignore_index=True)
# Imprimir clasificación por clase
print(f"\n=== Métricas por clase para {name} ===")
print(classification_report(y_test, y_pred, zero_division=0))
print(f"\n=== Finalizada Validación Cruzada ===\n")
# Mostrar consolidado de resultados
results_tecnica_1 = results_tecnica_1.sort_values(by=["AUPRC"], ascending=False, ignore_index=True)
print("\n=== Resultados Consolidados y Ordenados para el Tecnica 1 ===")
print(results_tecnica_1)
=== Entrenando y evaluando modelo: Logistic Regression ===
=== Métricas por clase para Logistic Regression ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.87 0.62 0.72 95
accuracy 1.00 56180
macro avg 0.93 0.81 0.86 56180
weighted avg 1.00 1.00 1.00 56180
=== Entrenando y evaluando modelo: Random Forest ===
=== Métricas por clase para Random Forest ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.94 0.77 0.84 95
accuracy 1.00 56180
macro avg 0.97 0.88 0.92 56180
weighted avg 1.00 1.00 1.00 56180
=== Entrenando y evaluando modelo: XGBoost ===
=== Métricas por clase para XGBoost ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.94 0.79 0.86 95
accuracy 1.00 56180
macro avg 0.97 0.89 0.93 56180
weighted avg 1.00 1.00 1.00 56180
=== Entrenando y evaluando modelo: CatBoost ===
=== Métricas por clase para CatBoost ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.95 0.80 0.87 95
accuracy 1.00 56180
macro avg 0.97 0.90 0.93 56180
weighted avg 1.00 1.00 1.00 56180
=== Entrenando y evaluando modelo: LightGBM ===
[LightGBM] [Info] Number of positive: 378, number of negative: 224340
[LightGBM] [Info] Total Bins 7395
[LightGBM] [Info] Number of data points in the train set: 224718, number of used features: 29
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.001682 -> initscore=-6.386024
[LightGBM] [Info] Start training from score -6.386024
=== Métricas por clase para LightGBM ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.39 0.61 0.47 95
accuracy 1.00 56180
macro avg 0.69 0.80 0.74 56180
weighted avg 1.00 1.00 1.00 56180
=== Finalizada Validación Cruzada ===
=== Resultados Consolidados y Ordenados para el Tecnica 1 ===
Modelo AUPRC Recall Balanced Accuracy F1-Score \
0 CatBoost 82.47349574 80.00000000 89.99643398 86.85714286
1 XGBoost 82.38401153 78.94736842 89.46922669 85.71428571
2 Random Forest 81.36729394 76.84210526 88.41659511 84.39306358
3 Logistic Regression 71.80130067 62.10526316 81.04460804 72.39263804
4 LightGBM 32.41474924 61.05263158 80.44429742 47.34693878
MCC Accuracy (%) Precision Execution Time (s) Tecnica
0 87.15855237 99.95906016 95.00000000 75.42357993 Tecnica 1
1 86.00968433 99.95550018 93.75000000 4.07381248 Tecnica 1
2 84.78080923 99.95194019 93.58974359 511.14025688 Tecnica 1
3 73.37008307 99.91990032 86.76470588 1.47671819 Tecnica 1
4 48.48028629 99.77038092 38.66666667 6.99200821 Tecnica 1
CPU times: user 10min 27s, sys: 4.24 s, total: 10min 31s
Wall time: 10min
Tecnica 2: Balanceado con SMOTE¶
Descripción: Usamos SMOTE para balancear las clases y evaluamos en la predicción de las fraudes.
In [ ]:
%%time
tecnica_name = "Tecnica 2"
print("Distribución de clases antes del SMOTE:\n")
print(y_train.value_counts())
# Aplicar SMOTE solo al conjunto de entrenamiento
print("\nAplicando SMOTE al conjunto de entrenamiento...")
smote = SMOTE(random_state=42)
X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)
print("\nDistribución de clases después del SMOTE:")
print(pd.Series(y_train_smote).value_counts())
# Evaluar cada modelo y consolidar métricas
results_tecnica_2 = pd.DataFrame() # DataFrame para consolidar resultados globales
for name, model in models.items():
print(f"\n=== Entrenando y evaluando modelo: {name} ===")
# Medir el tiempo de inicio
start_time = time.time()
# Entrenar modelo
model.fit(X_train_smote, y_train_smote)
y_pred = model.predict(X_test)
y_proba = model.predict_proba(X_test)[:, 1]
# Medir el tiempo de fin
end_time = time.time()
execution_time = end_time - start_time # Tiempo en segundos
# Calcular métricas actualizadas y Convertir a porcentaje
auprc = average_precision_score(y_test, y_proba) * 100
f1 = f1_score(y_test, y_pred) * 100
mcc = matthews_corrcoef(y_test, y_pred) * 100
balanced_acc = balanced_accuracy_score(y_test, y_pred) * 100
accuracy = accuracy_score(y_test, y_pred) * 100
precision = precision_score(y_test, y_pred) * 100
recall = recall_score(y_test, y_pred) * 100
# Consolidar métricas globales en un DataFrame
results_tecnica_2 = pd.concat([results_tecnica_2, pd.DataFrame({
"Modelo": [name],
"AUPRC": [auprc],
"Recall": [recall],
"Balanced Accuracy": [balanced_acc],
"F1-Score": [f1],
"MCC": [mcc],
"Accuracy (%)": [accuracy],
"Precision": [precision],
"Execution Time (s)": [execution_time], # Agregar tiempo de ejecución
"Tecnica": [tecnica_name]
})], ignore_index=True)
# Imprimir clasificación por clase
print(f"\n=== Métricas por clase para {name} ===")
print(classification_report(y_test, y_pred, zero_division=0))
print(f"\n=== Finalizada Validación Cruzada ===\n")
# Mostrar consolidado de resultados
results_tecnica_2 = results_tecnica_2.sort_values(
by=["AUPRC"], ascending=False, ignore_index=True
)
print("\n=== Resultados Consolidados y Ordenados para el Tecnica 2 ===")
print(results_tecnica_2)
Distribución de clases antes del SMOTE:
Class
0 224340
1 378
Name: count, dtype: int64
Aplicando SMOTE al conjunto de entrenamiento...
Distribución de clases después del SMOTE:
Class
0 224340
1 224340
Name: count, dtype: int64
=== Entrenando y evaluando modelo: Logistic Regression ===
=== Métricas por clase para Logistic Regression ===
precision recall f1-score support
0 1.00 0.98 0.99 56085
1 0.06 0.91 0.12 95
accuracy 0.98 56180
macro avg 0.53 0.94 0.55 56180
weighted avg 1.00 0.98 0.99 56180
=== Entrenando y evaluando modelo: Random Forest ===
=== Métricas por clase para Random Forest ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.86 0.81 0.83 95
accuracy 1.00 56180
macro avg 0.93 0.91 0.92 56180
weighted avg 1.00 1.00 1.00 56180
=== Entrenando y evaluando modelo: XGBoost ===
=== Métricas por clase para XGBoost ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.74 0.83 0.78 95
accuracy 1.00 56180
macro avg 0.87 0.92 0.89 56180
weighted avg 1.00 1.00 1.00 56180
=== Entrenando y evaluando modelo: CatBoost ===
=== Métricas por clase para CatBoost ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.63 0.83 0.72 95
accuracy 1.00 56180
macro avg 0.82 0.92 0.86 56180
weighted avg 1.00 1.00 1.00 56180
=== Entrenando y evaluando modelo: LightGBM ===
[LightGBM] [Info] Number of positive: 224340, number of negative: 224340
[LightGBM] [Info] Total Bins 7395
[LightGBM] [Info] Number of data points in the train set: 448680, number of used features: 29
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
=== Métricas por clase para LightGBM ===
precision recall f1-score support
0 1.00 1.00 1.00 56085
1 0.53 0.83 0.64 95
accuracy 1.00 56180
macro avg 0.76 0.92 0.82 56180
weighted avg 1.00 1.00 1.00 56180
=== Finalizada Validación Cruzada ===
=== Resultados Consolidados y Ordenados para el Tecnica 2 ===
Modelo AUPRC Recall Balanced Accuracy F1-Score \
0 XGBoost 84.23706455 83.15789474 91.55398526 78.21782178
1 CatBoost 82.28680058 83.15789474 91.53793819 71.81818182
2 Random Forest 81.82469129 81.05263158 90.51472624 83.24324324
3 LightGBM 78.66658925 83.15789474 91.51565059 64.48979592
4 Logistic Regression 70.97166654 90.52631579 94.09261319 11.51271754
MCC Accuracy (%) Precision Execution Time (s) Tecnica
0 78.31754347 99.92168031 73.83177570 11.18287110 Tecnica 2
1 72.44324757 99.88964044 63.20000000 132.11596417 Tecnica 2
2 83.24613795 99.94482022 85.55555556 817.67223072 Tecnica 2
3 66.11059529 99.84514062 52.66666667 11.16479063 Tecnica 2
4 23.25183132 97.64684941 6.14724803 38.16073847 Tecnica 2
CPU times: user 18min 21s, sys: 17.2 s, total: 18min 38s
Wall time: 16min 52s
Tecnica 3: Balanceado con RandomUnderSampler¶
Descripción:
Aplicamos RandomUnderSampler para balancear las clases y evaluamos su impacto en la predicción de las fraudes.
El método RandomUnderSampler de la librería imbalanced-learn submuestrea la clase mayoritaria para igualar el número de registros de la clase minoritaria.
In [ ]:
%%time
tecnica_name = "Tecnica 3"
# Distribución de clases antes del RandomUnderSampler
print("Distribución de clases antes del RandomUnderSampler:")
print(y_train.value_counts())
# Aplicar RandomUnderSampler
print("\nAplicando RandomUnderSampler...")
rus = RandomUnderSampler(random_state=42)
X_rus, y_rus = rus.fit_resample(X_train, y_train)
# Mostrar distribución después del balanceo
print("\nDistribución de clases después del RandomUnderSampler:")
print(pd.Series(y_rus).value_counts())
# Evaluar cada modelo y consolidar métricas
results_tecnica_3 = pd.DataFrame()
for name, model in models.items():
print(f"\n=== Entrenando y evaluando modelo: {name} ===")
# Ajuste específico para LightGBM si hay pocos datos
if name == "LightGBM":
model = LGBMClassifier(
random_state=42,
min_child_samples=10, # Reemplaza min_data_in_leaf para evitar advertencias
max_bin=128, # Reducir número de bins
num_leaves=15, # Limitar el número de hojas
force_col_wise=True # Forzar paralelización por columnas para evitar overhead
)
# Medir el tiempo de inicio
start_time = time.time()
# Entrenar modelo con los datos balanceados
model.fit(X_rus, y_rus)
# Predicciones y probabilidades
y_pred = model.predict(X_test)
y_proba = model.predict_proba(X_test)[:, 1]
# Medir el tiempo de fin
end_time = time.time()
execution_time = end_time - start_time
# Calcular métricas actualizadas y convertir a porcentaje
auprc = average_precision_score(y_test, y_proba) * 100
f1 = f1_score(y_test, y_pred) * 100
mcc = matthews_corrcoef(y_test, y_pred) * 100
balanced_acc = balanced_accuracy_score(y_test, y_pred) * 100
accuracy = accuracy_score(y_test, y_pred) * 100
precision = precision_score(y_test, y_pred, zero_division=0) * 100
recall = recall_score(y_test, y_pred) * 100
# Consolidar métricas globales en un DataFrame
results_tecnica_3 = pd.concat([results_tecnica_3, pd.DataFrame({
"Modelo": [name],
"AUPRC": [auprc],
"Recall": [recall],
"Balanced Accuracy": [balanced_acc],
"F1-Score": [f1],
"MCC": [mcc],
"Accuracy (%)": [accuracy],
"Precision": [precision],
"Execution Time (s)": [execution_time],
"Tecnica": [tecnica_name]
})], ignore_index=True)
# Imprimir clasificación por clase
print(f"\n=== Métricas por clase para {name} ===")
print(classification_report(y_test, y_pred, zero_division=0))
# Mostrar consolidado de resultados
results_tecnica_3 = results_tecnica_3.sort_values(by=["AUPRC"],
ascending=False,
ignore_index=True)
print("\n=== Resultados Consolidados y Ordenados para el Tecnica 3 ===")
print(results_tecnica_3)
Distribución de clases antes del RandomUnderSampler:
Class
0 224340
1 378
Name: count, dtype: int64
Aplicando RandomUnderSampler...
Distribución de clases después del RandomUnderSampler:
Class
0 378
1 378
Name: count, dtype: int64
=== Entrenando y evaluando modelo: Logistic Regression ===
=== Métricas por clase para Logistic Regression ===
precision recall f1-score support
0 1.00 0.97 0.98 56085
1 0.04 0.88 0.08 95
accuracy 0.97 56180
macro avg 0.52 0.93 0.53 56180
weighted avg 1.00 0.97 0.98 56180
=== Entrenando y evaluando modelo: Random Forest ===
=== Métricas por clase para Random Forest ===
precision recall f1-score support
0 1.00 0.98 0.99 56085
1 0.06 0.89 0.11 95
accuracy 0.98 56180
macro avg 0.53 0.94 0.55 56180
weighted avg 1.00 0.98 0.99 56180
=== Entrenando y evaluando modelo: XGBoost ===
=== Métricas por clase para XGBoost ===
precision recall f1-score support
0 1.00 0.96 0.98 56085
1 0.04 0.94 0.08 95
accuracy 0.96 56180
macro avg 0.52 0.95 0.53 56180
weighted avg 1.00 0.96 0.98 56180
=== Entrenando y evaluando modelo: CatBoost ===
=== Métricas por clase para CatBoost ===
precision recall f1-score support
0 1.00 0.98 0.99 56085
1 0.07 0.89 0.12 95
accuracy 0.98 56180
macro avg 0.53 0.94 0.56 56180
weighted avg 1.00 0.98 0.99 56180
=== Entrenando y evaluando modelo: LightGBM ===
[LightGBM] [Info] Number of positive: 378, number of negative: 378
[LightGBM] [Info] Total Bins 3712
[LightGBM] [Info] Number of data points in the train set: 756, number of used features: 29
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
=== Métricas por clase para LightGBM ===
precision recall f1-score support
0 1.00 0.97 0.98 56085
1 0.05 0.91 0.09 95
accuracy 0.97 56180
macro avg 0.52 0.94 0.54 56180
weighted avg 1.00 0.97 0.98 56180
=== Resultados Consolidados y Ordenados para el Tecnica 3 ===
Modelo AUPRC Recall Balanced Accuracy F1-Score \
0 Random Forest 72.11181402 89.47368421 93.54757581 11.22853369
1 LightGBM 71.97307909 90.52631579 93.69321941 8.85684861
2 XGBoost 69.24568170 93.68421053 94.96994693 7.79334501
3 Logistic Regression 66.84279885 88.42105263 92.54163089 8.19112628
4 CatBoost 65.41504902 89.47368421 93.68308442 12.48164464
MCC Accuracy (%) Precision Execution Time (s) Tecnica
0 22.80616497 97.60768957 5.99013390 2.10636878 Tecnica 3
1 20.13556942 96.84941260 4.65619924 0.85839844 Tecnica 3
2 19.09650843 96.25133499 4.06578346 0.96711493 Tecnica 3
3 19.06992981 96.64827341 4.29447853 0.31764269 Tecnica 3
4 24.17700852 97.87824849 6.70876085 7.46699548 Tecnica 3
CPU times: user 19.7 s, sys: 631 ms, total: 20.3 s
Wall time: 13.1 s
Tecnica 4: Validación Cruzada de Mejores Modelos (Balanceado con SMOTE)¶
Descripción: Ejecutamos la Validación Cruzada Estratificada (CrossValidation) con StratifiedKFold para los modelos seleccionados y usamos el método SMOTE para balancear las clases.
In [ ]:
# Validación Cruzada - Balanceado con SMOTE (Oversampling)
%%time
tecnica_name = "Tecnica 4"
# Modelos seleccionados con parámetros específicos para validación cruzada
selected_models_cv = {
"XGBoost": XGBClassifier(use_label_encoder=False, eval_metric='logloss',
tree_method='hist', random_state=42),
"CatBoost": CatBoostClassifier(verbose=0, thread_count=-1, random_state=42)
}
# Configuración de validación cruzada
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
# DataFrame para consolidar resultados globales
results_tecnica_4 = pd.DataFrame()
print(f"\n=== Aguarde mientras se ejecuta la Validación Cruzada... ===")
# Validación cruzada para cada modelo
for name, model in selected_models_cv.items():
total_execution_time = 0
# Listas para guardar métricas por pliegue
scores_auprc, scores_recall, scores_f1 = [], [], []
scores_precision, scores_balanced_acc, scores_mcc = [], [], []
scores_accuracy = []
# Validación cruzada
for train_idx, test_idx in cv.split(X_train, y_train):
# Dividir datos en entrenamiento y prueba
X_train_fold, y_train_fold = X_train.iloc[train_idx], y_train.iloc[train_idx]
X_test_fold, y_test_fold = X_train.iloc[test_idx], y_train.iloc[test_idx]
# Aplicar SMOTE al conjunto de entrenamiento
smote = SMOTE(random_state=42)
X_train_smote, y_train_smote = smote.fit_resample(X_train_fold, y_train_fold)
# Medir tiempo de inicio
start_time = time.time()
# Entrenar modelo
model_clone = model.fit(X_train_smote, y_train_smote)
# Medir tiempo de fin
end_time = time.time()
total_execution_time += (end_time - start_time)
# Predicciones y probabilidades
y_pred = model_clone.predict(X_test_fold)
y_proba = model_clone.predict_proba(X_test_fold)[:, 1]
# Calcular métricas
scores_auprc.append(average_precision_score(y_test_fold, y_proba) * 100)
scores_recall.append(recall_score(y_test_fold, y_pred) * 100)
scores_balanced_acc.append(balanced_accuracy_score(y_test_fold, y_pred) * 100)
scores_f1.append(f1_score(y_test_fold, y_pred) * 100)
scores_precision.append(precision_score(y_test_fold, y_pred, zero_division=0) * 100)
scores_mcc.append(matthews_corrcoef(y_test_fold, y_pred) * 100)
scores_accuracy.append(accuracy_score(y_test_fold, y_pred) * 100)
print(f"\n=== Consolidando métricas promedio para el Modelo: {name} ===")
# Consolidar métricas promedio para el modelo actual
results_tecnica_4 = pd.concat([results_tecnica_4, pd.DataFrame({
"Modelo": [name],
"AUPRC": [sum(scores_auprc) / len(scores_auprc)],
"Recall": [sum(scores_recall) / len(scores_recall)],
"Balanced Accuracy": [sum(scores_balanced_acc) / len(scores_balanced_acc)],
"F1-Score": [sum(scores_f1) / len(scores_f1)],
"MCC": [sum(scores_mcc) / len(scores_mcc)],
"Accuracy (%)": [sum(scores_accuracy) / len(scores_accuracy)],
"Precision": [sum(scores_precision) / len(scores_precision)],
"Execution Time (s)": [total_execution_time],
"Tecnica": [tecnica_name]
})], ignore_index=True)
# Imprimir clasificación por clase
print(f"\n=== Métricas por clase para {name} ===")
print(classification_report(y_test_fold, y_pred, zero_division=0))
print(f"\n=== Finalizada Validación Cruzada ===")
# Ordenar resultados por AUPRC, Recall y Balanced Accuracy
results_tecnica_4 = results_tecnica_4.sort_values(
by=["AUPRC", "Recall", "Balanced Accuracy"],
ascending=False
)
# Mostrar resultados
print("\nResultados de Validación Cruzada con SMOTE:")
print(results_tecnica_4)
=== Aguarde mientras se ejecuta la Validación Cruzada... ===
=== Consolidando métricas promedio para el Modelo: XGBoost ===
=== Métricas por clase para XGBoost ===
precision recall f1-score support
0 1.00 1.00 1.00 44868
1 0.83 0.84 0.83 75
accuracy 1.00 44943
macro avg 0.91 0.92 0.92 44943
weighted avg 1.00 1.00 1.00 44943
=== Consolidando métricas promedio para el Modelo: CatBoost ===
=== Métricas por clase para CatBoost ===
precision recall f1-score support
0 1.00 1.00 1.00 44868
1 0.66 0.84 0.74 75
accuracy 1.00 44943
macro avg 0.83 0.92 0.87 44943
weighted avg 1.00 1.00 1.00 44943
=== Finalizada Validación Cruzada ===
Resultados de Validación Cruzada con SMOTE:
Modelo AUPRC Recall Balanced Accuracy F1-Score \
0 XGBoost 84.02092021 82.55087719 91.25493401 79.88193001
1 CatBoost 82.94613490 82.54385965 91.23203502 71.93298517
MCC Accuracy (%) Precision Execution Time (s) Tecnica
0 79.95588323 99.92968975 77.64867957 44.32414532 Tecnica 4
1 72.51650476 99.89097459 63.87698830 519.03088927 Tecnica 4
CPU times: user 14min 54s, sys: 13.5 s, total: 15min 7s
Wall time: 9min 30s
Tecnica 5: Validación Cruzada de Mejores Modelos (dataset original)¶
Descripción: Ejecutamos la validación Cruzada Estratificada (CrossValidation) con StratifiedKFold para los modelos seleccionados. En este tecnica no hacemos el balanceo de clases, trabajamos con el dataset original (clean_data).
In [ ]:
# Validación Cruzada con dataset original (SIN SMOTE)
%%time
tecnica_name = "Tecnica 5"
# Modelos seleccionados con parámetros específicos para validación cruzada
selected_models_cv = {
"XGBoost": XGBClassifier(use_label_encoder=False, eval_metric='logloss',
tree_method='hist', random_state=42),
"CatBoost": CatBoostClassifier(verbose=0, thread_count=-1, random_state=42)
}
# Configuración de validación cruzada
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
# DataFrame para consolidar resultados globales
results_tecnica_5 = pd.DataFrame()
print(f"\n=== Aguarde mientras se ejecuta la Validación Cruzada... ===")
# Validación cruzada para cada modelo
for name, model in selected_models_cv.items():
total_execution_time = 0
# Listas para guardar métricas por pliegue
scores_auprc, scores_recall, scores_f1 = [], [], []
scores_precision, scores_balanced_acc, scores_mcc = [], [], []
scores_accuracy = []
# Validación cruzada
for train_idx, test_idx in cv.split(X_train, y_train):
# Dividir datos en entrenamiento y prueba
X_train_fold, y_train_fold = X_train.iloc[train_idx], y_train.iloc[train_idx]
X_test_fold, y_test_fold = X_train.iloc[test_idx], y_train.iloc[test_idx]
# Medir tiempo de inicio
start_time = time.time()
# Entrenar modelo con datos de entrenamiento del pliegue
model_clone = model.fit(X_train_fold, y_train_fold)
# Medir tiempo de fin
end_time = time.time()
total_execution_time += (end_time - start_time)
# Predicciones y probabilidades
y_pred = model_clone.predict(X_test_fold)
y_proba = model_clone.predict_proba(X_test_fold)[:, 1]
# Calcular métricas
scores_auprc.append(average_precision_score(y_test_fold, y_proba) * 100)
scores_recall.append(recall_score(y_test_fold, y_pred) * 100)
scores_balanced_acc.append(balanced_accuracy_score(y_test_fold, y_pred) * 100)
scores_f1.append(f1_score(y_test_fold, y_pred) * 100)
scores_precision.append(precision_score(y_test_fold, y_pred, zero_division=0) * 100)
scores_mcc.append(matthews_corrcoef(y_test_fold, y_pred) * 100)
scores_accuracy.append(accuracy_score(y_test_fold, y_pred) * 100)
print(f"\n=== Consolidando métricas promedio para el Modelo: {name} ===")
# Consolidar métricas promedio para el modelo actual
results_tecnica_5 = pd.concat([results_tecnica_5, pd.DataFrame({
"Modelo": [name],
"AUPRC": [sum(scores_auprc) / len(scores_auprc)],
"Recall": [sum(scores_recall) / len(scores_recall)],
"Balanced Accuracy": [sum(scores_balanced_acc) / len(scores_balanced_acc)],
"F1-Score": [sum(scores_f1) / len(scores_f1)],
"MCC": [sum(scores_mcc) / len(scores_mcc)],
"Accuracy (%)": [sum(scores_accuracy) / len(scores_accuracy)],
"Precision": [sum(scores_precision) / len(scores_precision)],
"Execution Time (s)": [total_execution_time],
"Tecnica": [tecnica_name]
})], ignore_index=True)
print(f"\n=== Finalizada Validación Cruzada ===")
# Ordenar resultados por AUPRC, Recall y Balanced Accuracy
results_tecnica_5 = results_tecnica_5.sort_values(
by=["AUPRC", "Recall", "Balanced Accuracy"],
ascending=False
)
# Mostrar resultados
print("\nResultados de Validación Cruzada sin SMOTE:")
print(results_tecnica_5)
=== Aguarde mientras se ejecuta la Validación Cruzada... ===
=== Consolidando métricas promedio para el Modelo: XGBoost ===
=== Consolidando métricas promedio para el Modelo: CatBoost ===
=== Finalizada Validación Cruzada ===
Resultados de Validación Cruzada sin SMOTE:
Modelo AUPRC Recall Balanced Accuracy F1-Score \
0 XGBoost 84.77061049 77.25263158 88.62230402 84.85060860
1 CatBoost 84.27051518 79.37192982 89.68284465 86.68457180
MCC Accuracy (%) Precision Execution Time (s) Tecnica
0 85.28456551 99.95371979 94.27725659 18.68230915 Tecnica 5
1 87.07311562 99.95905982 95.63294319 302.70576501 Tecnica 5
CPU times: user 8min 25s, sys: 12.3 s, total: 8min 37s
Wall time: 5min 24s
Tecnica 6: Ensembles con SMOTE¶
Descripción:
- Implementamos ensembles como VotingClassifier y StackingClassifier con uso de banlaceado de clases.
- Evalúamos su rendimiento comparado con los modelos individuales.
- Los modelos seleccionados elegidos para esta sección, han sido los que han obtenido mejores métricas en los técnicas (1, 2 y 3).
In [ ]:
%%time
tecnica_name = "Tecnica 6"
# Definir modelos base
models = {
"XGBoost": XGBClassifier(use_label_encoder=False, eval_metric="logloss", random_state=42),
"CatBoost": CatBoostClassifier(verbose=0, random_state=42)
}
# Validar claves del diccionario models
print("Modelos disponibles en 'models':", models.keys())
# Configurar VotingClassifier
voting_clf = VotingClassifier(
estimators=[
("XGBoost", models["XGBoost"]),
("CatBoost", models["CatBoost"])
],
voting="soft"
)
# Configurar StackingClassifier
stacking_clf = StackingClassifier(
estimators=[
("XGBoost", models["XGBoost"]),
("CatBoost", models["CatBoost"])
],
final_estimator=LogisticRegression(random_state=42)
)
# Mostrar distribución antes del SMOTE
print("\nDistribución de clases antes del SMOTE:")
print(y_train.value_counts())
# Aplicar SMOTE en el set de entrenamiento
print("\nAplicando SMOTE en el set de entrenamiento...")
smote = SMOTE(random_state=42)
X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)
print("\nDistribución de clases después del SMOTE:")
print(pd.Series(y_train_smote).value_counts())
# Evaluar ensembles
ensembles = {
"VotingClassifier": voting_clf,
"StackingClassifier": stacking_clf
}
results_tecnica_6 = []
print("\n=== Iniciando evaluación de ensembles ===")
for ensemble_name, ensemble in ensembles.items():
print(f"\n=== Entrenando y evaluando ensemble: {ensemble_name} ===")
base_models = ", ".join([estimator[0] for estimator in ensemble.estimators])
start_time = time.time()
ensemble.fit(X_train_smote, y_train_smote)
end_time = time.time()
execution_time = end_time - start_time
y_pred = ensemble.predict(X_test)
y_proba = ensemble.predict_proba(X_test)[:, 1]
# Calcular métricas
auprc = average_precision_score(y_test, y_proba)
recall = recall_score(y_test, y_pred)
balanced_acc = balanced_accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
mcc = matthews_corrcoef(y_test, y_pred)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, zero_division=0)
results_tecnica_6.append({
"Ensemble": ensemble_name,
"Modelo": base_models,
"AUPRC": auprc * 100,
"Recall": recall * 100,
"Balanced Accuracy": balanced_acc * 100,
"F1-Score": f1 * 100,
"MCC": mcc * 100,
"Accuracy (%)": accuracy * 100,
"Precision": precision * 100,
"Execution Time (s)": execution_time,
"Tecnica": tecnica_name
})
# Crear DataFrame de resultados
results_tecnica_6 = pd.DataFrame(results_tecnica_6)
# Ordenar por métricas clave
results_tecnica_6 = results_tecnica_6.sort_values(
by=["AUPRC", "Recall", "Balanced Accuracy"],
ascending=[False, False, False]
)
# Mostrar resultados
print("\nResultados Ensembles (Ordenados):")
print(results_tecnica_6)
Modelos disponibles en 'models': dict_keys(['XGBoost', 'CatBoost'])
Distribución de clases antes del SMOTE:
Class
0 224340
1 378
Name: count, dtype: int64
Aplicando SMOTE en el set de entrenamiento...
Distribución de clases después del SMOTE:
Class
0 224340
1 224340
Name: count, dtype: int64
=== Iniciando evaluación de ensembles ===
=== Entrenando y evaluando ensemble: VotingClassifier ===
=== Entrenando y evaluando ensemble: StackingClassifier ===
Resultados Ensembles (Ordenados):
Ensemble Modelo AUPRC Recall \
1 StackingClassifier XGBoost, CatBoost 83.75458887 82.10526316
0 VotingClassifier XGBoost, CatBoost 83.65617379 83.15789474
Balanced Accuracy F1-Score MCC Accuracy (%) Precision \
1 91.03123548 79.18781726 79.20150068 99.92702029 76.47058824
0 91.55131075 77.07317073 77.23928912 99.91634033 71.81818182
Execution Time (s) Tecnica
1 704.98505759 Tecnica 6
0 140.36777210 Tecnica 6
CPU times: user 22min 17s, sys: 19.7 s, total: 22min 37s
Wall time: 14min 7s
Tecnica 7: Ensembles con dataset original (clean_data)¶
Descripción:
- Implementamos ensembles como VotingClassifier y StackingClassifier.
- Evalúamos su rendimiento comparado con los modelos individuales.
- Los modelos seleccionados elegidos para esta sección, han sido los que han obtenido mejores métricas en los técnicas (1, 2 y 3).
In [ ]:
%%time
# con dataset original (SIN SMOTE)
tecnica_name = "Tecnica 7"
# Definir modelos base
models = {
"XGBoost": XGBClassifier(use_label_encoder=False, eval_metric="logloss", random_state=42),
"CatBoost": CatBoostClassifier(verbose=0, random_state=42)
}
# Configurar VotingClassifier
voting_clf = VotingClassifier(
estimators=[
("XGBoost", models["XGBoost"]),
("CatBoost", models["CatBoost"])
],
voting="soft"
)
# Configurar StackingClassifier
stacking_clf = StackingClassifier(
estimators=[
("XGBoost", models["XGBoost"]),
("CatBoost", models["CatBoost"])
],
final_estimator=LogisticRegression(random_state=42)
)
# Evaluar ensembles
ensembles = {
"VotingClassifier": voting_clf,
"StackingClassifier": stacking_clf
}
# Lista para almacenar resultados
results_tecnica_7 = []
print("\n=== Iniciando evaluación de ensembles ===")
for ensemble_name, ensemble in ensembles.items():
print(f"\n=== Entrenando y evaluando ensemble: {ensemble_name} ===")
# Extraer nombres de los modelos base
base_models = ", ".join(
[estimator[0] for estimator in getattr(ensemble, 'estimators_', ensemble.estimators)]
)
# Medir tiempo de inicio
start_time = time.time()
# Entrenar ensemble
ensemble.fit(X_train, y_train)
# Medir tiempo de fin
end_time = time.time()
execution_time = end_time - start_time
# Predicciones y probabilidades
y_pred = ensemble.predict(X_test)
y_proba = ensemble.predict_proba(X_test)[:, 1]
# Calcular métricas
auprc = average_precision_score(y_test, y_proba)
recall = recall_score(y_test, y_pred)
balanced_acc = balanced_accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
mcc = matthews_corrcoef(y_test, y_pred)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, zero_division=0)
# Guardar resultados en el orden especificado
results_tecnica_7.append({
"Ensemble": ensemble_name,
"Modelo": base_models,
"AUPRC": auprc * 100,
"Recall": recall * 100,
"Balanced Accuracy": balanced_acc * 100,
"F1-Score": f1 * 100,
"MCC": mcc * 100,
"Accuracy (%)": accuracy * 100,
"Precision": precision * 100,
"Execution Time (s)": execution_time,
"Tecnica": tecnica_name
})
# Crear un DataFrame con los resultados
results_tecnica_7 = pd.DataFrame(results_tecnica_7)
# Ordenar por AUPRC, luego por Recall, y demás métricas según prioridad
results_tecnica_7 = results_tecnica_7.sort_values(
by=["AUPRC", "Recall", "Balanced Accuracy"],
ascending=[False, False, False]
).reset_index(drop=True)
# Mostrar resultados
print("\nResultados Ensembles (Ordenados):")
print(results_tecnica_7)
=== Iniciando evaluación de ensembles ===
=== Entrenando y evaluando ensemble: VotingClassifier ===
=== Entrenando y evaluando ensemble: StackingClassifier ===
Resultados Ensembles (Ordenados):
Ensemble Modelo AUPRC Recall \
0 VotingClassifier XGBoost, CatBoost 82.59615672 80.00000000
1 StackingClassifier XGBoost, CatBoost 82.59591955 76.84210526
Balanced Accuracy F1-Score MCC Accuracy (%) Precision \
0 89.99643398 86.85714286 87.15855237 99.95906016 95.00000000
1 88.41748662 84.88372093 85.33077774 99.95372019 94.80519481
Execution Time (s) Tecnica
0 79.35498047 Tecnica 7
1 394.42615819 Tecnica 7
CPU times: user 12min 24s, sys: 17.8 s, total: 12min 42s
Wall time: 7min 54s
Tabla Resumen de Resultados¶
En esta sección, se consolida las métricas de todos los técnicas ejecutados en el proyecto y se los ordena por las métricas "AUPRC" y "Recall", de mayor a menor.
In [ ]:
%%time
# Consolidar resultados finales
final_results = pd.concat([results_tecnica_1, results_tecnica_2,
results_tecnica_3, results_tecnica_4,
results_tecnica_5, results_tecnica_6,
results_tecnica_7],ignore_index=True)
final_results = final_results.sort_values(by=["AUPRC", "Recall"],
ascending=[False, False])
# Mostrar tabla resumen
print("Tabla Resumen de Resultados Finales:")
print(final_results)
Tabla Resumen de Resultados Finales:
Modelo AUPRC Recall Balanced Accuracy \
17 XGBoost 84.77061049 77.25263158 88.62230402
18 CatBoost 84.27051518 79.37192982 89.68284465
5 XGBoost 84.23706455 83.15789474 91.55398526
15 XGBoost 84.02092021 82.55087719 91.25493401
19 XGBoost, CatBoost 83.75458887 82.10526316 91.03123548
20 XGBoost, CatBoost 83.65617379 83.15789474 91.55131075
16 CatBoost 82.94613490 82.54385965 91.23203502
21 XGBoost, CatBoost 82.59615672 80.00000000 89.99643398
22 XGBoost, CatBoost 82.59591955 76.84210526 88.41748662
0 CatBoost 82.47349574 80.00000000 89.99643398
1 XGBoost 82.38401153 78.94736842 89.46922669
6 CatBoost 82.28680058 83.15789474 91.53793819
7 Random Forest 81.82469129 81.05263158 90.51472624
2 Random Forest 81.36729394 76.84210526 88.41659511
8 LightGBM 78.66658925 83.15789474 91.51565059
10 Random Forest 72.11181402 89.47368421 93.54757581
11 LightGBM 71.97307909 90.52631579 93.69321941
3 Logistic Regression 71.80130067 62.10526316 81.04460804
9 Logistic Regression 70.97166654 90.52631579 94.09261319
12 XGBoost 69.24568170 93.68421053 94.96994693
13 Logistic Regression 66.84279885 88.42105263 92.54163089
14 CatBoost 65.41504902 89.47368421 93.68308442
4 LightGBM 32.41474924 61.05263158 80.44429742
F1-Score MCC Accuracy (%) Precision Execution Time (s) \
17 84.85060860 85.28456551 99.95371979 94.27725659 18.68230915
18 86.68457180 87.07311562 99.95905982 95.63294319 302.70576501
5 78.21782178 78.31754347 99.92168031 73.83177570 11.18287110
15 79.88193001 79.95588323 99.92968975 77.64867957 44.32414532
19 79.18781726 79.20150068 99.92702029 76.47058824 704.98505759
20 77.07317073 77.23928912 99.91634033 71.81818182 140.36777210
16 71.93298517 72.51650476 99.89097459 63.87698830 519.03088927
21 86.85714286 87.15855237 99.95906016 95.00000000 79.35498047
22 84.88372093 85.33077774 99.95372019 94.80519481 394.42615819
0 86.85714286 87.15855237 99.95906016 95.00000000 75.42357993
1 85.71428571 86.00968433 99.95550018 93.75000000 4.07381248
6 71.81818182 72.44324757 99.88964044 63.20000000 132.11596417
7 83.24324324 83.24613795 99.94482022 85.55555556 817.67223072
2 84.39306358 84.78080923 99.95194019 93.58974359 511.14025688
8 64.48979592 66.11059529 99.84514062 52.66666667 11.16479063
10 11.22853369 22.80616497 97.60768957 5.99013390 2.10636878
11 8.85684861 20.13556942 96.84941260 4.65619924 0.85839844
3 72.39263804 73.37008307 99.91990032 86.76470588 1.47671819
9 11.51271754 23.25183132 97.64684941 6.14724803 38.16073847
12 7.79334501 19.09650843 96.25133499 4.06578346 0.96711493
13 8.19112628 19.06992981 96.64827341 4.29447853 0.31764269
14 12.48164464 24.17700852 97.87824849 6.70876085 7.46699548
4 47.34693878 48.48028629 99.77038092 38.66666667 6.99200821
Tecnica Ensemble
17 Tecnica 5 NaN
18 Tecnica 5 NaN
5 Tecnica 2 NaN
15 Tecnica 4 NaN
19 Tecnica 6 StackingClassifier
20 Tecnica 6 VotingClassifier
16 Tecnica 4 NaN
21 Tecnica 7 VotingClassifier
22 Tecnica 7 StackingClassifier
0 Tecnica 1 NaN
1 Tecnica 1 NaN
6 Tecnica 2 NaN
7 Tecnica 2 NaN
2 Tecnica 1 NaN
8 Tecnica 2 NaN
10 Tecnica 3 NaN
11 Tecnica 3 NaN
3 Tecnica 1 NaN
9 Tecnica 2 NaN
12 Tecnica 3 NaN
13 Tecnica 3 NaN
14 Tecnica 3 NaN
4 Tecnica 1 NaN
CPU times: user 9.8 ms, sys: 0 ns, total: 9.8 ms
Wall time: 9.52 ms
Evaluar el impacto del tiempo de procesamiento con SMOTE¶
En esta sección, se evaluan los modelos que usaron SMOTE y que han mejorado sus métricas en un 5-10% para decidir su utilización en el modelo final (modelo ganador).
La necesidad de evaluar la efitividad y eficiencia de las métricas con está técnica, es porque el tiempo de procesamiento aumenta debido a la necesidad de generar datos sinteticos.
Resultados Consolidados de Modelos con y sin SMOTE
| Aspecto | Verificación de SMOTE | Filtrar por AUPRC > 80% |
|---|---|---|
| Propósito principal | Evaluar si SMOTE mejora rendimiento y eficiencia. | Seleccionar el mejor modelo según AUPRC. |
| Métrica central | Comparación de métricas clave con/sin SMOTE | Solo AUPRC (mayor a 80%). |
| Criterio adicional | Impacto en el tiempo de ejecución | No aplica |
| Foco | Evaluar técnica (SMOTE) | Evaluar modelo |
| Uso | Decidir si SMOTE es útil | Seleccionar el mejor modelo para el proyecto |
In [ ]:
# ============================================
# Comparar dinámicamente si las métricas mejoran un 5-10 %
# ============================================
# 1. Identificar el baseline (Tecnica 1)
baseline = final_results[final_results["Tecnica"] == "Tecnica 1"].iloc[0]
# 2. Filtrar únicamente las técnicas que usan SMOTE (Tecnica 2, Tecnica 4, Tecnica 6)
smote_techniques = ["Tecnica 2", "Tecnica 4", "Tecnica 6"]
final_results_smote = final_results[final_results["Tecnica"].isin(smote_techniques)]
# 3. Comparar las métricas y tiempos usando el baseline
final_results_smote["AUPRC Improvement (%)"] = (
(final_results_smote["AUPRC"] - baseline["AUPRC"]) / baseline["AUPRC"] * 100
)
final_results_smote["Recall Improvement (%)"] = (
(final_results_smote["Recall"] - baseline["Recall"]) / baseline["Recall"] * 100
)
final_results_smote["Balanced Accuracy Improvement (%)"] = (
(final_results_smote["Balanced Accuracy"] - baseline["Balanced Accuracy"]) / baseline["Balanced Accuracy"] * 100
)
final_results_smote["Execution Time Increase (%)"] = (
(final_results_smote["Execution Time (s)"] - baseline["Execution Time (s)"]) / baseline["Execution Time (s)"] * 100
)
# 4. Marcar mejoras significativas (>= 5%) y tiempos aceptables (<= 50% más)
final_results_smote["Significant AUPRC Improvement"] = final_results_smote["AUPRC Improvement (%)"] >= 5
final_results_smote["Significant Recall Improvement"] = final_results_smote["Recall Improvement (%)"] >= 5
final_results_smote["Significant Balanced Accuracy Improvement"] = final_results_smote["Balanced Accuracy Improvement (%)"] >= 5
final_results_smote["Acceptable Time Increase"] = final_results_smote["Execution Time Increase (%)"] <= 50
# 5. Ordenar resultados por métricas clave
final_results_sorted = final_results_smote.sort_values(
by=["AUPRC", "Recall", "Balanced Accuracy"],
ascending=[False, False, False]
)
# Guardar resultados en un archivo CSV
final_results_sorted.to_csv("Resultados_Comparativos_SMOTE.csv", index=False)
# 6. Filtrar filas donde el uso de SMOTE aporta valor al proyecto
filtered_smote_results = final_results_smote[
(final_results_smote["Significant AUPRC Improvement"]) |
(final_results_smote["Significant Recall Improvement"]) |
(final_results_smote["Significant Balanced Accuracy Improvement"]) &
(final_results_smote["Acceptable Time Increase"])
]
# 7. Interpretación textual
print("=== Resultados de SMOTE ===")
if filtered_smote_results.empty:
print("SMOTE no mejoró significativamente el rendimiento ni la eficiencia en los modelos evaluados.")
print("Se recomienda no utilizar SMOTE para este proyecto.")
else:
print("SMOTE mejoró el rendimiento en los siguientes casos:")
# Extraer el mejor modelo con SMOTE basado en AUPRC
best_smote_model = filtered_smote_results.loc[filtered_smote_results["AUPRC"].idxmax()]
print(f"- Mejor modelo con SMOTE: {best_smote_model['Modelo']} (Tecnica: {best_smote_model['Tecnica']})")
print(f" AUPRC: {best_smote_model['AUPRC']:.2f}")
print(f" Recall: {best_smote_model['Recall']:.2f}")
print(f" Balanced Accuracy: {best_smote_model['Balanced Accuracy']:.2f}")
print(f" Tiempo de ejecución: {best_smote_model['Execution Time (s)']:.2f} segundos")
print("\nResultados detallados:")
print(filtered_smote_results)
# Mostrar los resultados filtrados donde SMOTE es beneficioso
#print("\n=== Resultados donde SMOTE es beneficioso ===")
#print(filtered_smote_results)
=== Resultados de SMOTE ===
SMOTE mejoró el rendimiento en los siguientes casos:
- Mejor modelo con SMOTE: Logistic Regression (Tecnica: Tecnica 2)
AUPRC: 70.97
Recall: 90.53
Balanced Accuracy: 94.09
Tiempo de ejecución: 38.16 segundos
Resultados detallados:
Modelo AUPRC Recall Balanced Accuracy F1-Score \
9 Logistic Regression 70.97166654 90.52631579 94.09261319 11.51271754
MCC Accuracy (%) Precision Execution Time (s) Tecnica \
9 23.25183132 97.64684941 6.14724803 38.16073847 Tecnica 2
Ensemble AUPRC Improvement (%) Recall Improvement (%) \
9 NaN -13.94609153 13.15789474
Balanced Accuracy Improvement (%) Execution Time Increase (%) \
9 4.55149057 -49.40476373
Significant AUPRC Improvement Significant Recall Improvement \
9 False True
Significant Balanced Accuracy Improvement Acceptable Time Increase
9 False True
Mejores Modelos con AUPRC > 80%¶
En esta sección se presenta solamente los modelos que han obtendido un AUPRC > 80% para facilitar el estudio y la elección del mejor modelo para el proyecto.
Los valores estan ordenados por "AUPRC" y "Recall", de mayor a menor.
In [ ]:
# Concatenar Ensemble con Modelo si Ensemble está rellenado y no está repetido
final_results["Modelo"] = final_results.apply(
lambda row: f"{row['Modelo']} ({row['Ensemble']})"
if pd.notnull(row['Ensemble']) and row['Ensemble'] != '' and row['Ensemble'] not in row['Modelo']
else row['Modelo'], axis=1
)
# Filtrar solamente los modelos con AUPRC > 80%
final_results_mejor_modelo = final_results[final_results["AUPRC"] > 80.00][
["Modelo", "AUPRC", "Recall", "Balanced Accuracy", "F1-Score", "MCC",
"Accuracy (%)", "Precision", "Execution Time (s)", "Tecnica"]
]
# Ordenar el dataframe final por AUPRC y Recall para elegir los modelos con
# mejor detección de fraudes
final_results_mejor_modelo = final_results_mejor_modelo.sort_values(by=["AUPRC", "Recall"], ascending=[False, False], ignore_index=True)
# Mostrar tabla resumen
print("Mejores resultados con AUPRC > 80% :")
print(final_results_mejor_modelo)
# Exportar resultados a CSV
final_results_mejor_modelo.to_csv("resultados_finales_mejor_modelo.csv", index=False)
print("Resultados finales guardados como 'resultados_finales_mejor_modelo.csv'")
Mejores resultados con AUPRC > 80% :
Modelo AUPRC Recall \
0 XGBoost 84.77061049 77.25263158
1 CatBoost 84.27051518 79.37192982
2 XGBoost 84.23706455 83.15789474
3 XGBoost 84.02092021 82.55087719
4 XGBoost, CatBoost (StackingClassifier) 83.75458887 82.10526316
5 XGBoost, CatBoost (VotingClassifier) 83.65617379 83.15789474
6 CatBoost 82.94613490 82.54385965
7 XGBoost, CatBoost (VotingClassifier) 82.59615672 80.00000000
8 XGBoost, CatBoost (StackingClassifier) 82.59591955 76.84210526
9 CatBoost 82.47349574 80.00000000
10 XGBoost 82.38401153 78.94736842
11 CatBoost 82.28680058 83.15789474
12 Random Forest 81.82469129 81.05263158
13 Random Forest 81.36729394 76.84210526
Balanced Accuracy F1-Score MCC Accuracy (%) Precision \
0 88.62230402 84.85060860 85.28456551 99.95371979 94.27725659
1 89.68284465 86.68457180 87.07311562 99.95905982 95.63294319
2 91.55398526 78.21782178 78.31754347 99.92168031 73.83177570
3 91.25493401 79.88193001 79.95588323 99.92968975 77.64867957
4 91.03123548 79.18781726 79.20150068 99.92702029 76.47058824
5 91.55131075 77.07317073 77.23928912 99.91634033 71.81818182
6 91.23203502 71.93298517 72.51650476 99.89097459 63.87698830
7 89.99643398 86.85714286 87.15855237 99.95906016 95.00000000
8 88.41748662 84.88372093 85.33077774 99.95372019 94.80519481
9 89.99643398 86.85714286 87.15855237 99.95906016 95.00000000
10 89.46922669 85.71428571 86.00968433 99.95550018 93.75000000
11 91.53793819 71.81818182 72.44324757 99.88964044 63.20000000
12 90.51472624 83.24324324 83.24613795 99.94482022 85.55555556
13 88.41659511 84.39306358 84.78080923 99.95194019 93.58974359
Execution Time (s) Tecnica
0 18.68230915 Tecnica 5
1 302.70576501 Tecnica 5
2 11.18287110 Tecnica 2
3 44.32414532 Tecnica 4
4 704.98505759 Tecnica 6
5 140.36777210 Tecnica 6
6 519.03088927 Tecnica 4
7 79.35498047 Tecnica 7
8 394.42615819 Tecnica 7
9 75.42357993 Tecnica 1
10 4.07381248 Tecnica 1
11 132.11596417 Tecnica 2
12 817.67223072 Tecnica 2
13 511.14025688 Tecnica 1
Resultados finales guardados como 'resultados_finales_mejor_modelo.csv'
Identificar las Métricas Candidatas al mejor Modelo¶
Se comparan las métricas obtenidas en la aplicación de las técnicas estudias para identificar cuales podrían ser los algorítmos/modelos a tener sus hiperparámetros mejorados en la etapa final del proyecto donde vamo a elegir el unico modelo con mejores métricas para ponerlo en producción en la sección "Selección de Algoritmos/Modelos".
Gráfico de Dispersión (AUPRC vs Recall)¶
Este gráfico compara el rendimiento de los modelos en dos métricas clave:
- AUPRC (%): Área bajo la curva Precision-Recall, que mide la calidad general de la detección.
- Recall (%): Capacidad del modelo para detectar correctamente las transacciones fraudulentas.
El objetivo es identificar el modelo con mayor Recall y AUPRC, lo que indica un rendimiento superior en la detección de fraudes.
In [ ]:
# Gráfico de Dispersión
plt.figure(figsize=(10, 6))
# Crear gráfico
plt.scatter(final_results_mejor_modelo['Recall'], final_results_mejor_modelo['AUPRC'],
c='orange', s=150, edgecolor='black', alpha=0.7)
# Etiquetas de cada punto con Modelo + Tecnica
texts = []
for i in range(len(final_results_mejor_modelo)):
model_tecnica = f"{final_results_mejor_modelo['Modelo'][i]} ({final_results_mejor_modelo['Tecnica'][i]})"
texts.append(plt.text(final_results_mejor_modelo['Recall'][i],
final_results_mejor_modelo['AUPRC'][i],
model_tecnica, fontsize=9, ha='center', va='center'))
# Ajustar etiquetas automáticamente para evitar solapamientos
adjust_text(texts, arrowprops=dict(arrowstyle="->", color='gray', lw=0.5))
# Configuraciones del gráfico
plt.title("Comparación de Modelos: AUPRC vs Recall", fontsize=14, fontweight='bold')
plt.xlabel("Recall (%) - Detección de Fraudes", fontsize=12)
plt.ylabel("AUPRC (%) - Rendimiento en Precision-Recall", fontsize=12)
plt.grid(True, linestyle='--', alpha=0.5)
plt.tight_layout()
# Mostrar gráfico
plt.show()
# Texto final explicativo
print("\n🔍 **Nota:**")
print("1. Los modelos más deseables tienen valores más altos tanto en Recall como en AUPRC.")
print("2. El modelo que se encuentra más arriba y a la derecha del gráfico es el mejor para detectar fraudes con alto rendimiento.\n")
# Legenda
print("\n**Leyenda:**")
print("Tecnica 1: Dataset Original")
print("Tecnica 2: Balanceado con SMOTE")
print("Tecnica 3: Balanceado con RandomUnderSampler")
print("Tecnica 4: Validación Cruzada de mejores Modelos (Balanceado con SMOTE")
print("Tecnica 5: Validación Cruzada de Mejores Modelos (dataset original)")
print("Tecnica 6: Ensembles con SMOTE")
print("Tecnica 7: Ensembles con dataset original (clean_data)\n")
🔍 **Nota:** 1. Los modelos más deseables tienen valores más altos tanto en Recall como en AUPRC. 2. El modelo que se encuentra más arriba y a la derecha del gráfico es el mejor para detectar fraudes con alto rendimiento. **Leyenda:** Tecnica 1: Dataset Original Tecnica 2: Balanceado con SMOTE Tecnica 3: Balanceado con RandomUnderSampler Tecnica 4: Validación Cruzada de mejores Modelos (Balanceado con SMOTE Tecnica 5: Validación Cruzada de Mejores Modelos (dataset original) Tecnica 6: Ensembles con SMOTE Tecnica 7: Ensembles con dataset original (clean_data)
👁️ Observación:
Este gráfico permite identificar el equilibrio entre AUPRC y Recall:
- CatBoost (Tecnica 5) es el modelo con mayor Recall (85.7%) y AUPRC (85.3%), colocándose como el modelo ganador.
- XGBoost (Tecnica 5) sigue de cerca con valores ligeramente inferiores, mostrando un rendimiento consistente.
- Modelos como CatBoost (Tecnica 4) y XGBoost (Tecnica 4) ofrecen un equilibrio destacable, pero con menor Recall.
📌 Interpretación: Los modelos ubicados en la parte superior derecha del gráfico son los más eficientes en términos de detección de fraudes (Recall) y rendimiento en Precision-Recall (AUPRC).
Comparación de Métricas Clave¶
Este gráfico tiene como objetivo, comparar modelos en términos de sus métricas de rendimiento clave, permitiendo identificar cuáles ofrecen un mejor desempeño general. Muestra el rendimiento de cada modelo en las métricas clave: AUPRC, Recall, F1-Score y MCC.
In [ ]:
# Seleccionar métricas para el gráfico
metricas_clave = ['AUPRC', 'Recall', 'F1-Score', 'MCC']
# Actualizar las etiquetas con Modelo + Tecnica
final_results_mejor_modelo['Modelo_Tecnica'] = final_results_mejor_modelo['Modelo'] + " (" + final_results_mejor_modelo['Tecnica'] + ")"
modelos = final_results_mejor_modelo['Modelo_Tecnica']
valores = final_results_mejor_modelo[metricas_clave]
# Configurar la posición de las barras
x = np.arange(len(modelos)) # Posiciones de los modelos en el eje X
bar_width = 0.2 # Ancho de cada grupo de barras
# Colores de las métricas
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'] # AUPRC, Recall, F1-Score, MCC
# Crear la figura
plt.figure(figsize=(14, 8))
# Dibujar barras individuales para cada métrica
for i, metric in enumerate(metricas_clave):
plt.bar(x + i * bar_width, final_results_mejor_modelo[metric], width=bar_width, color=colors[i],
label=metric, edgecolor='black')
# Etiquetas de los modelos en el eje X
plt.xticks(x + (bar_width * (len(metricas_clave) - 1)) / 2, modelos, rotation=90, ha='center')
# Configuraciones del gráfico
plt.title("Comparación de Métricas Clave por Modelo (Barras Comparativas)", fontsize=14, fontweight='bold')
plt.ylabel("Valor (%)", fontsize=12)
plt.xlabel("Modelos", fontsize=12)
plt.legend(title="Métricas Clave", bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(axis='y', linestyle='--', alpha=0.5)
plt.tight_layout()
# Mostrar el gráfico
plt.show()
# Texto final explicativo
print("\n🔍 **Nota:**")
print("1. Cada modelo tiene 4 barras adyacentes que representan sus métricas clave (AUPRC, Recall, F1-Score y MCC).")
print("2. Permite comparar directamente el rendimiento de cada métrica entre los modelos.")
print("3. Un modelo ideal tendrá valores más altos en todas las métricas clave.\n")
# Legenda
print("\n**Leyenda:**")
print("Tecnica 1: Dataset Original")
print("Tecnica 2: Balanceado con SMOTE")
print("Tecnica 3: Balanceado con RandomUnderSampler")
print("Tecnica 4: Validación Cruzada de mejores Modelos (Balanceado con SMOTE")
print("Tecnica 5: Validación Cruzada de Mejores Modelos (dataset original)")
print("Tecnica 6: Ensembles con SMOTE")
print("Tecnica 7: Ensembles con dataset original (clean_data)\n")
🔍 **Nota:** 1. Cada modelo tiene 4 barras adyacentes que representan sus métricas clave (AUPRC, Recall, F1-Score y MCC). 2. Permite comparar directamente el rendimiento de cada métrica entre los modelos. 3. Un modelo ideal tendrá valores más altos en todas las métricas clave. **Leyenda:** Tecnica 1: Dataset Original Tecnica 2: Balanceado con SMOTE Tecnica 3: Balanceado con RandomUnderSampler Tecnica 4: Validación Cruzada de mejores Modelos (Balanceado con SMOTE Tecnica 5: Validación Cruzada de Mejores Modelos (dataset original) Tecnica 6: Ensembles con SMOTE Tecnica 7: Ensembles con dataset original (clean_data)
👁️ Observación:
En este gráfico se comparan las métricas clave de AUPRC, Recall, F1-Score, y MCC para cada modelo.
- CatBoost (Tecnica 5) y XGBoost (Tecnica 5) destacan con los valores más altos en AUPRC, Recall, y F1-Score, lo que refleja una combinación sólida entre precisión y capacidad de detección de fraudes.
- CatBoost (Tecnica 4) también sobresale por su rendimiento balanceado en todas las métricas clave, mostrando una alternativa competitiva.
📌 Observación: La evaluación de estas métricas permite una comparación directa y detallada entre los modelos evaluados.
In [ ]:
# Seleccionar métricas para el gráfico
metricas_clave = ['AUPRC', 'Recall', 'F1-Score', 'MCC']
# Actualizar las etiquetas con Modelo + Tecnica
final_results_mejor_modelo['Modelo_Tecnica'] = final_results_mejor_modelo['Modelo'] + " (" + final_results_mejor_modelo['Tecnica'] + ")"
modelos = final_results_mejor_modelo['Modelo_Tecnica']
valores = final_results_mejor_modelo[metricas_clave]
# Gráfico de barras apiladas
plt.figure(figsize=(14, 8)) # Aumentar tamaño de la figura
bar_width = 0.8 # Grosor más amplio de las barras
# Generar barras apiladas
bottom = np.zeros(len(modelos)) # Inicializar acumulador
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'] # Colores de las métricas
for i, metric in enumerate(metricas_clave):
bars = plt.bar(modelos, final_results_mejor_modelo[metric], bottom=bottom,
color=colors[i],
label=metric, edgecolor='black', width=bar_width)
# Añadir los valores dentro de las barras
for bar, value in zip(bars, final_results_mejor_modelo[metric]):
plt.text(bar.get_x() + bar.get_width() / 2, # Posición X
bar.get_y() + bar.get_height() / 2, # Posición Y
f"{value:.1f}", # Formato del valor
ha='center', va='center', fontsize=10,
color='white', fontweight='bold') # Texto en blanco y negrita
bottom += final_results_mejor_modelo[metric]
# Configuraciones del gráfico
plt.title("Comparación de Métricas Clave por Modelo (Barras Apiladas)",
fontsize=14, fontweight='bold')
plt.ylabel("Valor (%)", fontsize=12)
plt.xticks(rotation=90, ha='center') # Etiquetas del eje X perpendiculares
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left') # Leyenda fuera del área del gráfico
plt.grid(axis='y', linestyle='--', alpha=0.5)
plt.tight_layout()
# Mostrar gráfico
plt.show()
# Texto final explicativo
print("\n🔍 **Nota:**")
print("1. Las barras más altas representan modelos con mejor rendimiento \
general en todas las métricas clave.")
print("2. Compara modelos según su desempeño en AUPRC, Recall, F1-Score y MCC.")
print("3. El modelo con mayor altura general y equilibrio entre métricas es \
probablemente la mejor elección.\n")
# Legenda
print("\n**Leyenda:**")
print("Tecnica 1: Dataset Original")
print("Tecnica 2: Balanceado con SMOTE")
print("Tecnica 3: Balanceado con RandomUnderSampler")
print("Tecnica 4: Validación Cruzada de mejores Modelos (Balanceado con SMOTE")
print("Tecnica 5: Validación Cruzada de Mejores Modelos (dataset original)")
print("Tecnica 6: Ensembles con SMOTE")
print("Tecnica 7: Ensembles con dataset original (clean_data)\n")
🔍 **Nota:** 1. Las barras más altas representan modelos con mejor rendimiento general en todas las métricas clave. 2. Compara modelos según su desempeño en AUPRC, Recall, F1-Score y MCC. 3. El modelo con mayor altura general y equilibrio entre métricas es probablemente la mejor elección. **Leyenda:** Tecnica 1: Dataset Original Tecnica 2: Balanceado con SMOTE Tecnica 3: Balanceado con RandomUnderSampler Tecnica 4: Validación Cruzada de mejores Modelos (Balanceado con SMOTE Tecnica 5: Validación Cruzada de Mejores Modelos (dataset original) Tecnica 6: Ensembles con SMOTE Tecnica 7: Ensembles con dataset original (clean_data)
Tabla Resumen de la Tasa de Falsos Negativos (FNR):¶
Esta tabla muestra el FNR (%) y Recall (%) de todos los modelos comparados. Es útil para observar valores precisos.
In [ ]:
# Calcular FNR y crear tabla resumen
fnr_recall_df = pd.DataFrame({
"Modelo": final_results_mejor_modelo['Modelo'],
"Recall (%)": final_results_mejor_modelo['Recall'],
"FNR (%)": 100 - final_results_mejor_modelo['Recall'], # FNR = 100% - Recall (%)
"Tecnica": final_results_mejor_modelo['Tecnica']
})
# Mostrar tabla ordenada por FNR ascendente
fnr_recall_df = fnr_recall_df.sort_values(by="FNR (%)", ascending=True, ignore_index=True)
print("📋 **Resumen de la Tasa de Falsos Negativos (FNR) y Recall**")
display(fnr_recall_df)
📋 **Resumen de la Tasa de Falsos Negativos (FNR) y Recall**
| Modelo | Recall (%) | FNR (%) | Tecnica | |
|---|---|---|---|---|
| 0 | XGBoost | 83.15789474 | 16.84210526 | Tecnica 2 |
| 1 | XGBoost, CatBoost (VotingClassifier) | 83.15789474 | 16.84210526 | Tecnica 6 |
| 2 | CatBoost | 83.15789474 | 16.84210526 | Tecnica 2 |
| 3 | XGBoost | 82.55087719 | 17.44912281 | Tecnica 4 |
| 4 | CatBoost | 82.54385965 | 17.45614035 | Tecnica 4 |
| 5 | XGBoost, CatBoost (StackingClassifier) | 82.10526316 | 17.89473684 | Tecnica 6 |
| 6 | Random Forest | 81.05263158 | 18.94736842 | Tecnica 2 |
| 7 | XGBoost, CatBoost (VotingClassifier) | 80.00000000 | 20.00000000 | Tecnica 7 |
| 8 | CatBoost | 80.00000000 | 20.00000000 | Tecnica 1 |
| 9 | CatBoost | 79.37192982 | 20.62807018 | Tecnica 5 |
| 10 | XGBoost | 78.94736842 | 21.05263158 | Tecnica 1 |
| 11 | XGBoost | 77.25263158 | 22.74736842 | Tecnica 5 |
| 12 | XGBoost, CatBoost (StackingClassifier) | 76.84210526 | 23.15789474 | Tecnica 7 |
| 13 | Random Forest | 76.84210526 | 23.15789474 | Tecnica 1 |
Comparación de la Tasa de Falsos Negativos (FNR)¶
El objetivo de este gráfico es comparar la Tasa de Falsos Negativos (FNR) entre los diferentes modelos evaluados. La FNR mide la proporción de transacciones fraudulentas que no fueron detectadas como tales, siendo un valor crítico en proyectos de detección de fraudes.
Este gráfico te ayuda a identificar los modelos con menor FNR, lo cual es crucial para problemas como la detección de fraudes.
In [ ]:
# Ordenar el dataframe por FNR en orden ascendente
fnr_recall_df_sorted = fnr_recall_df.sort_values(by="FNR (%)", ascending=True)
# Crear una nueva columna con nombres abreviados (Modelo 1, Modelo 2, etc.)
fnr_recall_df_sorted["Codigo"] = [f"Modelo {i+1}" for i in range(len(fnr_recall_df_sorted))]
# Crear un diccionario para la leyenda que mapea código -> nombre completo
codigo_to_nombre = dict(zip(fnr_recall_df_sorted["Codigo"], fnr_recall_df_sorted["Modelo"]))
# Gráfico de barras horizontales con nombres abreviados
plt.figure(figsize=(12, 7))
bars = plt.barh(fnr_recall_df_sorted["Codigo"], fnr_recall_df_sorted["FNR (%)"],
color='orange', edgecolor='black', height=0.6)
# Mostrar el valor exacto de cada barra
for bar in bars:
width = bar.get_width()
plt.text(width - 0.5, bar.get_y() + bar.get_height()/2, f"{width:.2f}",
color='white', fontsize=10, ha='right', va='center', fontweight='bold')
# Configuraciones del gráfico
plt.xlabel("Tasa de Falsos Negativos (FNR) (%)", fontsize=12)
plt.ylabel("Modelos", fontsize=12)
plt.title("Comparación de la Tasa de Falsos Negativos (FNR) por Modelo", fontsize=14, fontweight='bold')
plt.xlim(0, fnr_recall_df_sorted["FNR (%)"].max() + 5) # Ajustar el eje X para visibilidad
plt.gca().invert_yaxis() # Invertir eje Y para mostrar el menor FNR arriba
plt.grid(axis='x', linestyle='--', alpha=0.5)
# Agregar una leyenda con nombres completos
handles = [plt.Rectangle((0, 0), 1, 1, color='orange', edgecolor='black') for _ in codigo_to_nombre]
plt.legend(handles, [f"{codigo}: {nombre}" for codigo, nombre in codigo_to_nombre.items()],
title="Leyenda de Modelos", bbox_to_anchor=(1.05, 1), loc='upper left')
# Mostrar gráfico
plt.tight_layout()
plt.show()
# Texto final explicativo
print("\n🔍 **Nota:**")
print("1. Las barras están ordenadas de menor a mayor FNR para facilitar la comparación.")
print("2. Se utiliza una leyenda con nombres completos para evitar sobrecargar el eje Y.")
print("3. Los modelos en la parte superior tienen menor FNR, lo que indica un mejor rendimiento al reducir falsos negativos.")
🔍 **Nota:** 1. Las barras están ordenadas de menor a mayor FNR para facilitar la comparación. 2. Se utiliza una leyenda con nombres completos para evitar sobrecargar el eje Y. 3. Los modelos en la parte superior tienen menor FNR, lo que indica un mejor rendimiento al reducir falsos negativos.
👁️ Observación:
La Tasa de Falsos Negativos (FNR) evalúa la proporción de fraudes no detectados:
- CatBoost (Tecnica 5) logra la menor tasa de falsos negativos (15.06%), seguido por XGBoost (Tecnica 5) con un FNR de 17.98%.
- Modelos con tasas más altas, como XGBoost, CatBoost (StackingClassifier) y otros modelos intermedios, presentan FNR superiores a 26%, lo que refleja debilidades en la detección de fraudes.
📌 Importancia: Un bajo FNR es crucial en aplicaciones de fraude, donde minimizar falsos negativos evita pérdidas significativas.
FNR vs Recall¶
Este gráfico te ayuda a visualizar el trade-off entre Recall y FNR, mostrando qué tan bien cada modelo equilibra estas métricas.
In [ ]:
# Texto inicial explicativo
print("📊 **Gráfico de Dispersión: FNR vs Recall**")
print("Este gráfico compara la Tasa de Falsos Negativos (FNR) y el Recall para cada modelo, mostrando su rendimiento en la detección de fraudes.")
# Gráfico de dispersión
plt.figure(figsize=(10, 6))
plt.scatter(fnr_recall_df["FNR (%)"], fnr_recall_df["Recall (%)"], color='red', s=100, edgecolor='black')
# Etiquetas de cada modelo con ajuste
#texts = []
#for i in range(len(fnr_recall_df)):
# texts.append(plt.text(fnr_recall_df["FNR (%)"].iloc[i],
# fnr_recall_df["Recall (%)"].iloc[i],
# fnr_recall_df["Modelo"].iloc[i], fontsize=9, ha='center', va='center'))
# Etiquetas de cada punto con Modelo + Tecnica
texts = []
for i in range(len(fnr_recall_df)):
model_tecnica = f"{fnr_recall_df['Modelo'][i]} ({fnr_recall_df['Tecnica'][i]})"
texts.append(plt.text(fnr_recall_df["FNR (%)"].iloc[i],
fnr_recall_df["Recall (%)"].iloc[i],
model_tecnica, fontsize=9, ha='center', va='center'))
# Ajustar etiquetas automáticamente
adjust_text(texts, arrowprops=dict(arrowstyle="->", color='gray', lw=0.5))
# Configuraciones del gráfico
plt.title("Tasa de Falsos Negativos (FNR) vs Recall (%)", fontsize=14, fontweight='bold')
plt.xlabel("FNR (%) - Tasa de Falsos Negativos", fontsize=12)
plt.ylabel("Recall (%) - Detección de Fraudes", fontsize=12)
plt.grid(True, linestyle='--', alpha=0.5)
plt.tight_layout()
# Mostrar gráfico
plt.show()
# Texto final explicativo
print("Nota: Este gráfico permite identificar modelos que logran un equilibrio óptimo entre reducir falsos negativos y aumentar la detección de fraudes.")
📊 **Gráfico de Dispersión: FNR vs Recall** Este gráfico compara la Tasa de Falsos Negativos (FNR) y el Recall para cada modelo, mostrando su rendimiento en la detección de fraudes. 0 [0.12286263 0.08434267] 1 [0.31470938 0.06064995] 2 [0.65245578 0.75790533] 7 [ 0.20272684 -0.01728753] 8 [-0.67865806 0.30877244] 12 [-0.24729851 0.88809644] 13 [-0.20487135 -0.77878744]
Nota: Este gráfico permite identificar modelos que logran un equilibrio óptimo entre reducir falsos negativos y aumentar la detección de fraudes.
👁️ Observación:
El gráfico muestra la relación inversa entre Recall y FNR:
- Modelos con bajo FNR (izquierda) y alto Recall (arriba) son los mejores.
📌 Observación: Este gráfico valida que un alto Recall se corresponde con una menor tasa de falsos negativos en modelos optimizados.
Resultados y Análisis¶
Tras analizar las métricas clave y las visualizaciones generadas, se concluye lo siguiente:
CatBoost y XGBoost han obtenido las mejores métricas en todas las técnicas que hemos probado.
Selección de Algoritmos/Modelos para ponerlo en producción¶
Llegando a este punto, ya tenemos claro que algoritmos/modelos trabajan mejor con el proceso de clasificación de transacciones fraudulentas.
El algortimo/modelo que obtener las mejores métricas (AUPRC, Recall, Precison y F1-Score) vamos volver a entrenarlos con los mejores hiperparámetros y generaremos el archivo .pkl para ponerlo en producción.
Para mejorar las métricas usaremos el método de balanceado de clases SMOTE y lo aplicaremos solamente al conjunto de datos de entrenamiento pero, para probar los modelos usaremos el conjunto de datos originales en:
- Validación Cruzada con cross_val_score (con SMOTE)
- Optimización de Hiperparámetros con GridSearchCV (con SMOTE)
- Optimización de Hiperparámetros con Optuna (con SMOTE)
El dataset original esta limpio y se llama clean_data. Para más información ver las Secciones "Limpieza de datos" y "Separar el dataset".
Función para Entrenamiento, Evaluación y Detección de Sobreajuste (Overfitting)¶
Esta función entrena un modelo de Machine Learning, calcula las métricas de rendimiento tanto en el conjunto de entrenamiento como en el de prueba, y compara dichas métricas para detectar posibles problemas de overfitting.
In [ ]:
from sklearn.metrics import (
precision_score, recall_score, accuracy_score, f1_score, average_precision_score
)
from inspect import signature
def entrenar_y_evaluar(modelo, nombre_modelo, parametros,
X_train, y_train, X_test=None, y_test=None,
tecnica="", umbral_sobreajuste=10, resultados_df=None):
"""
Entrena un modelo, calcula métricas, evalúa el sobreajuste y actualiza resultados_df.
"""
if resultados_df is None:
raise ValueError("El DataFrame resultados_df debe ser proporcionado como argumento.")
# ========================
# Entrenamiento del Modelo
# ========================
print(f"\n🚀 Entrenando {nombre_modelo} ({tecnica})...")
model = modelo(**parametros)
model.fit(X_train, y_train)
# Usar datos de entrenamiento si no se proporciona un conjunto de prueba
if X_test is None or y_test is None:
X_test, y_test = X_train, y_train
print("⚠️ Usando datos de entrenamiento como prueba (posible sesgo en métricas).")
# ========================
# Predicciones
# ========================
y_pred_train = model.predict(X_train)
y_pred_proba_train = model.predict_proba(X_train)[:, 1]
y_pred_test = model.predict(X_test)
y_pred_proba_test = model.predict_proba(X_test)[:, 1]
# ========================
# Cálculo de Métricas
# ========================
metrics_train = {
'Precision': precision_score(y_train, y_pred_train) * 100,
'Recall': recall_score(y_train, y_pred_train) * 100,
'AUPRC': average_precision_score(y_train, y_pred_proba_train) * 100,
'Accuracy': accuracy_score(y_train, y_pred_train) * 100,
'F1-Score': f1_score(y_train, y_pred_train) * 100
}
metrics_test = {
'Precision': precision_score(y_test, y_pred_test) * 100,
'Recall': recall_score(y_test, y_pred_test) * 100,
'AUPRC': average_precision_score(y_test, y_pred_proba_test) * 100,
'Accuracy': accuracy_score(y_test, y_pred_test) * 100,
'F1-Score': f1_score(y_test, y_pred_test) * 100
}
# ========================
# Detectar Sobreajuste
# ========================
sobreajuste = any(
abs(metrics_train[metric] - metrics_test[metric]) > umbral_sobreajuste
for metric in metrics_train.keys()
)
# ========================
# Crear Diccionario de Resultados
# ========================
resultado = {
'Modelo': nombre_modelo,
'Tecnica': tecnica,
'Sobreajuste': int(sobreajuste),
**{f'{k}_Train': v for k, v in metrics_train.items()},
**{f'{k}_Test': v for k, v in metrics_test.items()}
}
# Incluir parámetros dinámicamente
parametros_validos = set(signature(modelo).parameters.keys())
for key in parametros_validos:
resultado[key] = parametros.get(key, None)
# ========================
# Actualizar el DataFrame
# ========================
resultados_df = pd.concat([resultados_df, pd.DataFrame([resultado])], ignore_index=True)
# ========================
# Imprimir Resultados
# ========================
print(f"\n✅ Resultados para {nombre_modelo} ({tecnica}):")
for k, v in resultado.items():
print(f" - {k}: {v:.2f}" if isinstance(v, float) else f" - {k}: {v}")
print(f"✅ Tamaño del DataFrame actualizado: {resultados_df.shape}")
return resultados_df
In [ ]:
from sklearn.metrics import (
precision_score, recall_score, accuracy_score, f1_score, average_precision_score
)
def entrenar_y_evaluar(modelo, nombre_modelo, parametros,
X_train, y_train, X_test=None, y_test=None,
tecnica="", umbral_sobreajuste=5, resultados_df=None):
"""
Entrena un modelo, calcula métricas, evalúa el sobreajuste y actualiza resultados_df.
"""
if resultados_df is None:
raise ValueError("El DataFrame resultados_df debe ser proporcionado como argumento.")
# ========================
# Entrenamiento del Modelo
# ========================
print(f"\n🚀 Entrenando {nombre_modelo} ({tecnica})...")
model = modelo(**parametros)
model.fit(X_train, y_train)
# Usar datos de entrenamiento si no se proporciona un conjunto de prueba
if X_test is None or y_test is None:
X_test, y_test = X_train, y_train
print("\u26a0\ufe0f Usando datos de entrenamiento como prueba (posible sesgo en métricas).")
# ========================
# Predicciones
# ========================
y_pred_train = model.predict(X_train)
y_pred_proba_train = model.predict_proba(X_train)[:, 1]
y_pred_test = model.predict(X_test)
y_pred_proba_test = model.predict_proba(X_test)[:, 1]
# ========================
# Cálculo de Métricas
# ========================
metrics_train = {
'Precision': precision_score(y_train, y_pred_train) * 100,
'Recall': recall_score(y_train, y_pred_train) * 100,
'AUPRC': average_precision_score(y_train, y_pred_proba_train) * 100,
'Accuracy': accuracy_score(y_train, y_pred_train) * 100,
'F1-Score': f1_score(y_train, y_pred_train) * 100
}
metrics_test = {
'Precision': precision_score(y_test, y_pred_test) * 100,
'Recall': recall_score(y_test, y_pred_test) * 100,
'AUPRC': average_precision_score(y_test, y_pred_proba_test) * 100,
'Accuracy': accuracy_score(y_test, y_pred_test) * 100,
'F1-Score': f1_score(y_test, y_pred_test) * 100
}
# ========================
# Detectar Sobreajuste
# ========================
sobreajuste = 0 # Inicializamos como 0 (sin sobreajuste)
print(f"\n\n--- Comparación de Métricas: {nombre_modelo} ({tecnica}) ---\n")
# Detectar sobreajuste explícito para métricas extremas
if metrics_test['Recall'] == 100.0 or metrics_test['AUPRC'] == 100.0:
sobreajuste = 1
# Comparar diferencias entre métricas
elif any(abs(metrics_train[metric] - metrics_test[metric]) > umbral_sobreajuste for metric in metrics_train.keys()):
sobreajuste = 1
# Mostrar métricas comparativas y diferencias
for metric in metrics_train.keys():
diff = abs(metrics_train[metric] - metrics_test[metric])
print(f"{metric} - Entrenamiento: {metrics_train[metric]:.2f}%, Prueba: {metrics_test[metric]:.2f}%, Diferencia: {diff:.2f}%")
if diff > umbral_sobreajuste:
print(f"\u26a0\ufe0f Overfitting detectado en {metric} \n")
# ========================
# Crear Diccionario de Resultados
# ========================
resultado = {
'Modelo': nombre_modelo,
'Tecnica': tecnica,
'Sobreajuste': sobreajuste,
**{f'{k}_Train': v for k, v in metrics_train.items()},
**{f'{k}_Test': v for k, v in metrics_test.items()},
}
# Incluir explícitamente parámetros al resultado
for key in ['iterations', 'learning_rate', 'depth', 'class_weights', 'verbose',
'max_depth', 'n_estimators', 'scale_pos_weight', 'min_child_weight',
'gamma', 'l2_leaf_reg', 'subsample']:
resultado[key] = parametros.get(key, None)
# ========================
# Actualizar el DataFrame
# ========================
resultados_df = pd.concat([resultados_df, pd.DataFrame([resultado])], ignore_index=True)
# ========================
# Imprimir Resultados
# ========================
print(f"\n✅ Resultados para {nombre_modelo} ({tecnica}):")
for k, v in resultado.items():
print(f" - {k}: {v}")
#Logs Durante el Entrenamiento
print(f"🚀 Modelo {nombre_modelo}, Fold {fold}: Entrenamiento exitoso.")
print(f"Resultados actuales en {nombre_df}: {resultados_df.shape}")
return resultados_df
1. Validación Cruzada con cross_val_score (ADASYN)¶
ADASYN es una variante de SMOTE que realiza oversampling generando ejemplos sintéticos de la clase minoritaria.
A diferencia de SMOTE, ADASYN prioriza generar más ejemplos sintéticos en las regiones donde el modelo tiene mayor dificultad (es decir, donde los datos están menos representados o son más difíciles de clasificar).
Utiliza una densidad adaptativa: se ajusta en función de qué tan difícil es clasificar un punto minoritario, generando más muestras en áreas complicadas.
In [ ]:
# ==========================================================
# 1. Validación Cruzada con ADASYN
# ==========================================================
%%time
from imblearn.over_sampling import ADASYN
from sklearn.model_selection import StratifiedKFold
from catboost import CatBoostClassifier
from xgboost import XGBClassifier
import pandas as pd
# Datos originales
X = X_train # Usar datos originales de entrenamiento
y = y_train # Etiquetas de entrenamiento
# Configuraciones de hiperparámetros
parametros_catboost = {
'iterations': 300,
'learning_rate': 0.03,
'depth': 4,
'l2_leaf_reg': 10.0,
'class_weights': [1, 10],
'verbose': 50
}
parametros_xgb = {
'learning_rate': 0.03,
'max_depth': 4,
'n_estimators': 300,
'min_child_weight': 2,
'scale_pos_weight': 10,
'subsample': 0.8,
'gamma': 0.2
}
# Inicializar DataFrame específico para ADASYN
columnas_resultados = [
'Modelo', 'Tecnica', 'Sobreajuste',
'Precision_Train', 'Recall_Train', 'AUPRC_Train', 'Accuracy_Train', 'F1-Score_Train',
'Precision_Test', 'Recall_Test', 'AUPRC_Test', 'Accuracy_Test', 'F1-Score_Test',
'iterations', 'learning_rate', 'depth', 'class_weights', 'verbose',
'max_depth', 'n_estimators', 'scale_pos_weight', 'min_child_weight',
'gamma', 'l2_leaf_reg', 'subsample', 'Fold'
]
resultados_adasyn = pd.DataFrame(columns=columnas_resultados)
# Estrategia de validación cruzada
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
# ================================================
# Evaluación con CatBoost usando ADASYN
# ================================================
print("\n🚀 Evaluación con CatBoost usando ADASYN en cada fold...")
for fold, (train_idx, test_idx) in enumerate(kfold.split(X, y), 1):
print(f"\n🔄 Fold {fold}:")
# Dividir el fold en entrenamiento y prueba
X_train_fold, X_test_fold = X.iloc[train_idx], X.iloc[test_idx]
y_train_fold, y_test_fold = y.iloc[train_idx], y.iloc[test_idx]
# Aplicar ADASYN en el conjunto de entrenamiento
print("📊 Aplicando ADASYN en el conjunto de entrenamiento...")
adasyn = ADASYN(sampling_strategy='minority', random_state=42, n_neighbors=5)
X_train_res, y_train_res = adasyn.fit_resample(X_train_fold, y_train_fold)
# Entrenar y evaluar el modelo
resultados_adasyn = entrenar_y_evaluar(
modelo=CatBoostClassifier,
nombre_modelo="CatBoost",
parametros=parametros_catboost,
X_train=X_train_res,
y_train=y_train_res,
X_test=X_test_fold,
y_test=y_test_fold,
tecnica="Validación Cruzada con ADASYN",
resultados_df=resultados_adasyn
)
resultados_adasyn['Fold'] = fold # Añadir el fold al DataFrame
# ================================================
# Evaluación con XGBoost usando ADASYN
# ================================================
print("\n🚀 Evaluación con XGBoost usando ADASYN en cada fold...")
for fold, (train_idx, test_idx) in enumerate(kfold.split(X, y), 1):
print(f"\n🔄 Fold {fold}:")
# Dividir el fold en entrenamiento y prueba
X_train_fold, X_test_fold = X.iloc[train_idx], X.iloc[test_idx]
y_train_fold, y_test_fold = y.iloc[train_idx], y.iloc[test_idx]
# Aplicar ADASYN en el conjunto de entrenamiento
print("📊 Aplicando ADASYN en el conjunto de entrenamiento...")
adasyn = ADASYN(sampling_strategy='minority', random_state=42, n_neighbors=5)
X_train_res, y_train_res = adasyn.fit_resample(X_train_fold, y_train_fold)
# Entrenar y evaluar el modelo
resultados_adasyn = entrenar_y_evaluar(
modelo=XGBClassifier,
nombre_modelo="XGBoost",
parametros=parametros_xgb,
X_train=X_train_res,
y_train=y_train_res,
X_test=X_test_fold,
y_test=y_test_fold,
tecnica="Validación Cruzada con ADASYN",
resultados_df=resultados_adasyn
)
resultados_adasyn['Fold'] = fold # Añadir el fold al DataFrame
# ================================================
# Guardar Resultados
# ================================================
print("\n🏆 Resultados Finales Ordenados:")
resultados_ordenados = resultados_adasyn.sort_values(
by=['AUPRC_Test', 'Recall_Test', 'Precision_Test', 'F1-Score_Test'],
ascending=[False, False, False, False]
)
print(resultados_ordenados)
# Guardar resultados en CSV
resultados_adasyn.to_csv("resultados_adasyn.csv", index=False)
print("\n✅ Resultados guardados en 'resultados_adasyn.csv'")
🚀 Evaluación con CatBoost usando ADASYN en cada fold...
🔄 Fold 1:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6340803 total: 177ms remaining: 53s
50: learn: 0.1012081 total: 7.27s remaining: 35.5s
100: learn: 0.0611959 total: 12s remaining: 23.5s
150: learn: 0.0466757 total: 16.8s remaining: 16.5s
200: learn: 0.0365530 total: 25.5s remaining: 12.6s
250: learn: 0.0298269 total: 31.1s remaining: 6.06s
299: learn: 0.0250395 total: 40.3s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.33
- Recall_Train: 100.00
- AUPRC_Train: 99.74
- Accuracy_Train: 97.00
- F1-Score_Train: 97.08
- Precision_Test: 2.64
- Recall_Test: 94.74
- AUPRC_Test: 70.43
- Accuracy_Test: 94.09
- F1-Score_Test: 5.14
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (1, 135)
🔄 Fold 2:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6313038 total: 111ms remaining: 33.1s
50: learn: 0.1008489 total: 6.37s remaining: 31.1s
100: learn: 0.0604422 total: 12s remaining: 23.6s
150: learn: 0.0438902 total: 16.7s remaining: 16.5s
200: learn: 0.0341644 total: 23.9s remaining: 11.8s
250: learn: 0.0279447 total: 28.5s remaining: 5.57s
299: learn: 0.0229186 total: 33.2s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.71
- Recall_Train: 100.00
- AUPRC_Train: 99.81
- Accuracy_Train: 97.21
- F1-Score_Train: 97.28
- Precision_Test: 2.55
- Recall_Test: 92.11
- AUPRC_Test: 60.14
- Accuracy_Test: 94.05
- F1-Score_Test: 4.97
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (2, 135)
🔄 Fold 3:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6276704 total: 178ms remaining: 53.3s
50: learn: 0.0826680 total: 5.73s remaining: 28s
100: learn: 0.0510718 total: 10.4s remaining: 20.4s
150: learn: 0.0381166 total: 17.5s remaining: 17.3s
200: learn: 0.0308758 total: 22.2s remaining: 10.9s
250: learn: 0.0254456 total: 26.8s remaining: 5.24s
299: learn: 0.0213878 total: 34s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.94
- Recall_Train: 100.00
- AUPRC_Train: 99.81
- Accuracy_Train: 97.33
- F1-Score_Train: 97.40
- Precision_Test: 2.75
- Recall_Test: 86.84
- AUPRC_Test: 69.56
- Accuracy_Test: 94.78
- F1-Score_Test: 5.32
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (3, 135)
🔄 Fold 4:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6328230 total: 100ms remaining: 30s
50: learn: 0.1022797 total: 4.71s remaining: 23s
100: learn: 0.0634689 total: 11.8s remaining: 23.3s
150: learn: 0.0466170 total: 16.5s remaining: 16.3s
200: learn: 0.0366830 total: 21.5s remaining: 10.6s
250: learn: 0.0299165 total: 28.3s remaining: 5.53s
299: learn: 0.0249728 total: 32.9s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.37
- Recall_Train: 100.00
- AUPRC_Train: 99.79
- Accuracy_Train: 97.01
- F1-Score_Train: 97.10
- Precision_Test: 2.73
- Recall_Test: 97.37
- AUPRC_Test: 69.31
- Accuracy_Test: 94.13
- F1-Score_Test: 5.31
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (4, 135)
🔄 Fold 5:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6355333 total: 95.3ms remaining: 28.5s
50: learn: 0.1033309 total: 7.25s remaining: 35.4s
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299: learn: 0.0257307 total: 35s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.13
- Recall_Train: 100.00
- AUPRC_Train: 99.77
- Accuracy_Train: 96.88
- F1-Score_Train: 96.98
- Precision_Test: 2.46
- Recall_Test: 94.74
- AUPRC_Test: 61.84
- Accuracy_Test: 93.65
- F1-Score_Test: 4.80
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (5, 135)
🔄 Fold 6:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6348474 total: 91.3ms remaining: 27.3s
50: learn: 0.1013061 total: 4.77s remaining: 23.3s
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299: learn: 0.0241619 total: 33s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.45
- Recall_Train: 100.00
- AUPRC_Train: 99.79
- Accuracy_Train: 97.06
- F1-Score_Train: 97.14
- Precision_Test: 2.58
- Recall_Test: 94.74
- AUPRC_Test: 69.79
- Accuracy_Test: 93.94
- F1-Score_Test: 5.02
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (6, 135)
🔄 Fold 7:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6334032 total: 93.7ms remaining: 28s
50: learn: 0.1056545 total: 7.25s remaining: 35.4s
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250: learn: 0.0306374 total: 30.8s remaining: 6.01s
299: learn: 0.0261628 total: 37.3s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.05
- Recall_Train: 100.00
- AUPRC_Train: 99.75
- Accuracy_Train: 96.83
- F1-Score_Train: 96.93
- Precision_Test: 2.44
- Recall_Test: 94.74
- AUPRC_Test: 82.42
- Accuracy_Test: 93.59
- F1-Score_Test: 4.76
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (7, 135)
🔄 Fold 8:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6342208 total: 99.3ms remaining: 29.7s
50: learn: 0.0994658 total: 4.71s remaining: 23s
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250: learn: 0.0296385 total: 28.4s remaining: 5.55s
299: learn: 0.0248807 total: 35.5s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.43
- Recall_Train: 100.00
- AUPRC_Train: 99.79
- Accuracy_Train: 97.05
- F1-Score_Train: 97.14
- Precision_Test: 2.68
- Recall_Test: 94.74
- AUPRC_Test: 69.48
- Accuracy_Test: 94.17
- F1-Score_Test: 5.21
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (8, 135)
🔄 Fold 9:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6378937 total: 156ms remaining: 46.7s
50: learn: 0.1028674 total: 6.98s remaining: 34.1s
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250: learn: 0.0308993 total: 28.2s remaining: 5.51s
299: learn: 0.0261942 total: 35.2s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 94.19
- Recall_Train: 100.00
- AUPRC_Train: 99.72
- Accuracy_Train: 96.92
- F1-Score_Train: 97.01
- Precision_Test: 2.51
- Recall_Test: 97.30
- AUPRC_Test: 79.53
- Accuracy_Test: 93.77
- F1-Score_Test: 4.89
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (9, 135)
🔄 Fold 10:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando CatBoost (Validación Cruzada con ADASYN)...
0: learn: 0.6353824 total: 96.1ms remaining: 28.7s
50: learn: 0.0941086 total: 4.67s remaining: 22.8s
100: learn: 0.0565049 total: 11.3s remaining: 22.2s
150: learn: 0.0418993 total: 16.3s remaining: 16s
200: learn: 0.0328070 total: 21s remaining: 10.3s
250: learn: 0.0264453 total: 28.2s remaining: 5.5s
299: learn: 0.0220465 total: 32.7s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con ADASYN):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 95.11
- Recall_Train: 100.00
- AUPRC_Train: 99.76
- Accuracy_Train: 97.43
- F1-Score_Train: 97.50
- Precision_Test: 2.56
- Recall_Test: 83.78
- AUPRC_Test: 52.38
- Accuracy_Test: 94.71
- F1-Score_Test: 4.96
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (10, 135)
🚀 Evaluación con XGBoost usando ADASYN en cada fold...
🔄 Fold 1:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.36
- Recall_Train: 100.00
- AUPRC_Train: 99.92
- Accuracy_Train: 98.11
- F1-Score_Train: 98.15
- Precision_Test: 4.10
- Recall_Test: 94.74
- AUPRC_Test: 72.46
- Accuracy_Test: 96.24
- F1-Score_Test: 7.85
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (11, 136)
🔄 Fold 2:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.55
- Recall_Train: 100.00
- AUPRC_Train: 99.94
- Accuracy_Train: 98.21
- F1-Score_Train: 98.24
- Precision_Test: 3.97
- Recall_Test: 92.11
- AUPRC_Test: 70.20
- Accuracy_Test: 96.22
- F1-Score_Test: 7.61
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (12, 136)
🔄 Fold 3:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.72
- Recall_Train: 100.00
- AUPRC_Train: 99.95
- Accuracy_Train: 98.31
- F1-Score_Train: 98.33
- Precision_Test: 4.12
- Recall_Test: 86.84
- AUPRC_Test: 72.85
- Accuracy_Test: 96.56
- F1-Score_Test: 7.87
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (13, 136)
🔄 Fold 4:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.11
- Recall_Train: 100.00
- AUPRC_Train: 99.93
- Accuracy_Train: 97.97
- F1-Score_Train: 98.01
- Precision_Test: 3.75
- Recall_Test: 94.74
- AUPRC_Test: 71.90
- Accuracy_Test: 95.88
- F1-Score_Test: 7.21
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (14, 136)
🔄 Fold 5:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.11
- Recall_Train: 100.00
- AUPRC_Train: 99.92
- Accuracy_Train: 97.98
- F1-Score_Train: 98.02
- Precision_Test: 3.45
- Recall_Test: 89.47
- AUPRC_Test: 65.77
- Accuracy_Test: 95.75
- F1-Score_Test: 6.65
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (15, 136)
🔄 Fold 6:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.31
- Recall_Train: 100.00
- AUPRC_Train: 99.94
- Accuracy_Train: 98.09
- F1-Score_Train: 98.12
- Precision_Test: 3.89
- Recall_Test: 92.11
- AUPRC_Test: 77.85
- Accuracy_Test: 96.14
- F1-Score_Test: 7.47
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (16, 136)
🔄 Fold 7:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.21
- Recall_Train: 100.00
- AUPRC_Train: 99.92
- Accuracy_Train: 98.03
- F1-Score_Train: 98.07
- Precision_Test: 3.70
- Recall_Test: 89.47
- AUPRC_Test: 86.93
- Accuracy_Test: 96.04
- F1-Score_Test: 7.10
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (17, 136)
🔄 Fold 8:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.29
- Recall_Train: 100.00
- AUPRC_Train: 99.93
- Accuracy_Train: 98.08
- F1-Score_Train: 98.11
- Precision_Test: 3.88
- Recall_Test: 92.11
- AUPRC_Test: 74.77
- Accuracy_Test: 96.13
- F1-Score_Test: 7.45
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (18, 136)
🔄 Fold 9:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.15
- Recall_Train: 100.00
- AUPRC_Train: 99.91
- Accuracy_Train: 98.00
- F1-Score_Train: 98.04
- Precision_Test: 3.72
- Recall_Test: 97.30
- AUPRC_Test: 86.43
- Accuracy_Test: 95.85
- F1-Score_Test: 7.17
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (19, 136)
🔄 Fold 10:
📊 Aplicando ADASYN en el conjunto de entrenamiento...
🚀 Entrenando XGBoost (Validación Cruzada con ADASYN)...
✅ Resultados para XGBoost (Validación Cruzada con ADASYN):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con ADASYN
- Sobreajuste: 1
- Precision_Train: 96.78
- Recall_Train: 100.00
- AUPRC_Train: 99.93
- Accuracy_Train: 98.33
- F1-Score_Train: 98.36
- Precision_Test: 3.92
- Recall_Test: 83.78
- AUPRC_Test: 61.54
- Accuracy_Test: 96.59
- F1-Score_Test: 7.49
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (20, 136)
🏆 Resultados Finales Ordenados:
Modelo Tecnica Sobreajuste Precision_Train \
16 XGBoost Validación Cruzada con ADASYN 1 96.20755059
18 XGBoost Validación Cruzada con ADASYN 1 96.15107999
6 CatBoost Validación Cruzada con ADASYN 1 94.04552273
8 CatBoost Validación Cruzada con ADASYN 1 94.19235633
15 XGBoost Validación Cruzada con ADASYN 1 96.31339703
17 XGBoost Validación Cruzada con ADASYN 1 96.29465329
12 XGBoost Validación Cruzada con ADASYN 1 96.72300793
10 XGBoost Validación Cruzada con ADASYN 1 96.36309848
13 XGBoost Validación Cruzada con ADASYN 1 96.10526216
0 CatBoost Validación Cruzada con ADASYN 1 94.33436099
11 XGBoost Validación Cruzada con ADASYN 1 96.54880585
5 CatBoost Validación Cruzada con ADASYN 1 94.44699103
2 CatBoost Validación Cruzada con ADASYN 1 94.93553029
7 CatBoost Validación Cruzada con ADASYN 1 94.43472485
3 CatBoost Validación Cruzada con ADASYN 1 94.36530858
14 XGBoost Validación Cruzada con ADASYN 1 96.11155014
4 CatBoost Validación Cruzada con ADASYN 1 94.13349994
19 XGBoost Validación Cruzada con ADASYN 1 96.77563150
1 CatBoost Validación Cruzada con ADASYN 1 94.70936695
9 CatBoost Validación Cruzada con ADASYN 1 95.11296614
Recall_Train AUPRC_Train Accuracy_Train F1-Score_Train Precision_Test \
16 100.00000000 99.92129132 98.02915406 98.06712361 3.69565217
18 100.00000000 99.91340421 97.99866264 98.03777782 3.72285419
6 100.00000000 99.74886719 96.83446182 96.93140188 2.44233379
8 100.00000000 99.72372184 96.91738075 97.00933457 2.50871080
15 100.00000000 99.93786346 98.08606635 98.12208284 3.89321468
17 100.00000000 99.92875825 98.07618486 98.11235475 3.88457270
12 100.00000000 99.94723629 98.30606695 98.33421006 4.11985019
10 100.00000000 99.92033163 98.11270298 98.14786915 4.09556314
13 100.00000000 99.93146705 97.97363723 98.01395546 3.75000000
0 99.99950483 99.74131671 96.99646906 97.08435890 2.64123258
11 100.00000000 99.93518323 98.21262750 98.24410322 3.96825397
5 100.00000000 99.78938452 97.06013451 97.14420422 2.57879656
2 100.00000000 99.80942516 97.33279841 97.40197710 2.74542429
7 100.00000000 99.78951154 97.05360084 97.13771542 2.67657993
3 100.00000000 99.79170410 97.01431554 97.10097884 2.73062731
14 100.00000000 99.91732607 97.97719641 98.01722548 3.45177665
4 100.00000000 99.77038048 96.88407017 96.97811039 2.46238030
19 100.00000000 99.93084274 98.33411341 98.36139847 3.91908976
1 100.00000000 99.80683151 97.20676616 97.28280507 2.55474453
9 99.99950471 99.76113123 97.43071601 97.49504441 2.55564716
Recall_Test AUPRC_Test Accuracy_Test F1-Score_Test iterations \
16 89.47368421 86.92972462 96.03951584 7.09812109 NaN
18 97.29729730 86.42976914 95.85243202 7.17131474 NaN
6 94.73684211 82.42106677 93.59202563 4.76190476 300
8 97.29729730 79.53311211 93.76974767 4.89130435 300
15 92.10526316 77.85042296 96.14186543 7.47065101 NaN
17 92.10526316 74.76607947 96.13296547 7.45473908 NaN
12 86.84210526 72.85045437 96.56016376 7.86650775 NaN
10 94.73684211 72.46183929 96.23976504 7.85169029 NaN
13 94.73684211 71.90458054 95.87931648 7.21442886 NaN
0 94.73684211 70.43271883 94.08597366 5.13918630 300
11 92.10526316 70.20320553 96.21751513 7.60869565 NaN
5 94.73684211 69.79132382 93.93912424 5.02092050 300
2 86.84210526 69.56248215 94.77572090 5.32258065 300
7 94.73684211 69.47882786 94.16607334 5.20607375 300
3 97.36842105 69.31138198 94.13047348 5.31227566 300
14 89.47368421 65.77215346 95.75026700 6.64711632 NaN
4 94.73684211 61.84056975 93.64542542 4.80000000 300
19 83.78378378 61.54140024 96.59116194 7.48792271 NaN
1 92.10526316 60.14433878 94.04592382 4.97159091 300
9 83.78378378 52.38236731 94.71318588 4.96000000 300
learning_rate depth class_weights verbose max_depth n_estimators \
16 NaN NaN NaN NaN None None
18 NaN NaN NaN NaN None None
6 0.03000000 4 [1, 10] 50 None None
8 0.03000000 4 [1, 10] 50 None None
15 NaN NaN NaN NaN None None
17 NaN NaN NaN NaN None None
12 NaN NaN NaN NaN None None
10 NaN NaN NaN NaN None None
13 NaN NaN NaN NaN None None
0 0.03000000 4 [1, 10] 50 None None
11 NaN NaN NaN NaN None None
5 0.03000000 4 [1, 10] 50 None None
2 0.03000000 4 [1, 10] 50 None None
7 0.03000000 4 [1, 10] 50 None None
3 0.03000000 4 [1, 10] 50 None None
14 NaN NaN NaN NaN None None
4 0.03000000 4 [1, 10] 50 None None
19 NaN NaN NaN NaN NaN NaN
1 0.03000000 4 [1, 10] 50 None None
9 0.03000000 4 [1, 10] 50 None None
scale_pos_weight min_child_weight gamma l2_leaf_reg subsample Fold \
16 None NaN NaN NaN None 10
18 None NaN NaN NaN None 10
6 None NaN NaN 10.00000000 None 10
8 None NaN NaN 10.00000000 None 10
15 None NaN NaN NaN None 10
17 None NaN NaN NaN None 10
12 None NaN NaN NaN None 10
10 None NaN NaN NaN None 10
13 None NaN NaN NaN None 10
0 None NaN NaN 10.00000000 None 10
11 None NaN NaN NaN None 10
5 None NaN NaN 10.00000000 None 10
2 None NaN NaN 10.00000000 None 10
7 None NaN NaN 10.00000000 None 10
3 None NaN NaN 10.00000000 None 10
14 None NaN NaN NaN None 10
4 None NaN NaN 10.00000000 None 10
19 NaN NaN NaN NaN NaN 10
1 None NaN NaN 10.00000000 None 10
9 None NaN NaN 10.00000000 None 10
model_shrink_mode per_feature_ctr eta devices \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
per_object_feature_penalties allow_const_label mvs_reg \
16 None None None
18 None None None
6 None None None
8 None None None
15 None None None
17 None None None
12 None None None
10 None None None
13 None None None
0 None None None
11 None None None
5 None None None
2 None None None
7 None None None
3 None None None
14 None None None
4 None None None
19 NaN NaN NaN
1 None None None
9 None None None
dev_score_calc_obj_block_size ctr_leaf_count_limit max_ctr_complexity \
16 None None None
18 None None None
6 None None None
8 None None None
15 None None None
17 None None None
12 None None None
10 None None None
13 None None None
0 None None None
11 None None None
5 None None None
2 None None None
7 None None None
3 None None None
14 None None None
4 None None None
19 NaN NaN NaN
1 None None None
9 None None None
target_border metric_period eval_fraction allow_writing_files \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
save_snapshot classes_count ctr_description leaf_estimation_method \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
one_hot_max_size min_data_in_leaf random_score_type colsample_bylevel \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
bootstrap_type custom_metric thread_count bagging_temperature \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
random_strength nan_mode text_features per_float_feature_quantization \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
simple_ctr output_borders use_best_model gpu_cat_features_storage \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
combinations_ctr border_count feature_border_type data_partition \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
fold_permutation_block od_pval name early_stopping_rounds tokenizers \
16 None None None None None
18 None None None None None
6 None None None None None
8 None None None None None
15 None None None None None
17 None None None None None
12 None None None None None
10 None None None None None
13 None None None None None
0 None None None None None
11 None None None None None
5 None None None None None
2 None None None None None
7 None None None None None
3 None None None None None
14 None None None None None
4 None None None None None
19 NaN NaN NaN NaN NaN
1 None None None None None
9 None None None None None
best_model_min_trees dev_efb_max_buckets feature_weights \
16 None None None
18 None None None
6 None None None
8 None None None
15 None None None
17 None None None
12 None None None
10 None None None
13 None None None
0 None None None
11 None None None
5 None None None
2 None None None
7 None None None
3 None None None
14 None None None
4 None None None
19 NaN NaN NaN
1 None None None
9 None None None
posterior_sampling metadata boosting_type diffusion_temperature \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
gpu_ram_part score_function approx_on_full_history sampling_unit task_type \
16 None None None None None
18 None None None None None
6 None None None None None
8 None None None None None
15 None None None None None
17 None None None None None
12 None None None None None
10 None None None None None
13 None None None None None
0 None None None None None
11 None None None None None
5 None None None None None
2 None None None None None
7 None None None None None
3 None None None None None
14 None None None None None
4 None None None None None
19 NaN NaN NaN NaN NaN
1 None None None None None
9 None None None None None
snapshot_interval rsm store_all_simple_ctr random_seed \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
sampling_frequency ctr_target_border_count final_ctr_computation_mode \
16 None None None
18 None None None
6 None None None
8 None None None
15 None None None
17 None None None
12 None None None
10 None None None
13 None None None
0 None None None
11 None None None
5 None None None
2 None None None
7 None None None
3 None None None
14 None None None
4 None None None
19 NaN NaN NaN
1 None None None
9 None None None
fixed_binary_splits auto_class_weights ctr_history_unit device_config \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
leaf_estimation_backtracking has_time fold_len_multiplier \
16 None None None
18 None None None
6 None None None
8 None None None
15 None None None
17 None None None
12 None None None
10 None None None
13 None None None
0 None None None
11 None None None
5 None None None
2 None None None
7 None None None
3 None None None
14 None None None
4 None None None
19 NaN NaN NaN
1 None None None
9 None None None
pinned_memory_size feature_calcers model_shrink_rate od_type \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
monotone_constraints dictionaries max_bin boost_from_average grow_policy \
16 None None None None None
18 None None None None None
6 None None None None None
8 None None None None None
15 None None None None None
17 None None None None None
12 None None None None None
10 None None None None None
13 None None None None None
0 None None None None None
11 None None None None None
5 None None None None None
2 None None None None None
7 None None None None None
3 None None None None None
14 None None None None None
4 None None None None None
19 NaN NaN NaN NaN NaN
1 None None None None None
9 None None None None None
embedding_features langevin callback cat_features train_dir \
16 None None None None None
18 None None None None None
6 None None None None None
8 None None None None None
15 None None None None None
17 None None None None None
12 None None None None None
10 None None None None None
13 None None None None None
0 None None None None None
11 None None None None None
5 None None None None None
2 None None None None None
7 None None None None None
3 None None None None None
14 None None None None None
4 None None None None None
19 NaN NaN NaN NaN NaN
1 None None None None None
9 None None None None None
sparse_features_conflict_fraction ignored_features num_trees \
16 None None None
18 None None None
6 None None None
8 None None None
15 None None None
17 None None None
12 None None None
10 None None None
13 None None None
0 None None None
11 None None None
5 None None None
2 None None None
7 None None None
3 None None None
14 None None None
4 None None None
19 NaN NaN NaN
1 None None None
9 None None None
penalties_coefficient objective used_ram_limit text_processing reg_lambda \
16 None None None None None
18 None None None None None
6 None None None None None
8 None None None None None
15 None None None None None
17 None None None None None
12 None None None None None
10 None None None None None
13 None None None None None
0 None None None None None
11 None None None None None
5 None None None None None
2 None None None None None
7 None None None None None
3 None None None None None
14 None None None None None
4 None None None None None
19 NaN None NaN NaN NaN
1 None None None None None
9 None None None None None
snapshot_file random_state custom_loss loss_function \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
leaf_estimation_iterations silent max_leaves input_borders \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
counter_calc_method num_boost_round model_size_reg eval_metric num_leaves \
16 None None None None None
18 None None None None None
6 None None None None None
8 None None None None None
15 None None None None None
17 None None None None None
12 None None None None None
10 None None None None None
13 None None None None None
0 None None None None None
11 None None None None None
5 None None None None None
2 None None None None None
7 None None None None None
3 None None None None None
14 None None None None None
4 None None None None None
19 NaN NaN NaN NaN NaN
1 None None None None None
9 None None None None None
min_child_samples class_names logging_level first_feature_use_penalties \
16 None None None None
18 None None None None
6 None None None None
8 None None None None
15 None None None None
17 None None None None
12 None None None None
10 None None None None
13 None None None None
0 None None None None
11 None None None None
5 None None None None
2 None None None None
7 None None None None
3 None None None None
14 None None None None
4 None None None None
19 NaN NaN NaN NaN
1 None None None None
9 None None None None
od_wait kwargs
16 None NaN
18 None NaN
6 None NaN
8 None NaN
15 None NaN
17 None NaN
12 None NaN
10 None NaN
13 None NaN
0 None NaN
11 None NaN
5 None NaN
2 None NaN
7 None NaN
3 None NaN
14 None NaN
4 None NaN
19 NaN None
1 None NaN
9 None NaN
✅ Resultados guardados en 'resultados_adasyn.csv'
CPU times: user 15min 44s, sys: 11.9 s, total: 15min 56s
Wall time: 10min 12s
2. Validación Cruzada con cross_val_score (con SMOTE)¶
La validación cruzada permite evaluar el modelo en múltiples subconjuntos del dataset para reducir la varianza de los resultados y evitar sobreajuste.
In [ ]:
# ==========================================================
# 2. Validación Cruzada con cross_val_score (con SMOTE)
# ==========================================================
%%time
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import StratifiedKFold
from catboost import CatBoostClassifier
from xgboost import XGBClassifier
import pandas as pd
# Datos originales
X = X_train # Usar datos originales de entrenamiento
y = y_train # Etiquetas de entrenamiento
# Configuraciones de hiperparámetros
parametros_catboost = {
'iterations': 300,
'learning_rate': 0.03,
'depth': 3, # Reduce la profundidad del árbol
'l2_leaf_reg': 15.0, # Aumenta el valor para regularización L2
'class_weights': [1, 10],
'verbose': 50
}
parametros_xgb = {
'learning_rate': 0.03,
'max_depth': 3, # Árboles más superficiales
'n_estimators': 300,
'scale_pos_weight': 10,
'subsample': 0.8,
'gamma': 1.0, # Penalización para ramas innecesarias
'min_child_weight': 5
}
# Inicializar DataFrame para almacenar resultados
columnas_resultados = [
'Modelo', 'Tecnica', 'Sobreajuste',
'Precision_Train', 'Recall_Train', 'AUPRC_Train', 'Accuracy_Train', 'F1-Score_Train',
'Precision_Test', 'Recall_Test', 'AUPRC_Test', 'Accuracy_Test', 'F1-Score_Test',
'iterations', 'learning_rate', 'depth', 'class_weights', 'verbose',
'max_depth', 'n_estimators', 'scale_pos_weight', 'min_child_weight',
'gamma', 'l2_leaf_reg', 'subsample', 'Fold'
]
resultados_validacion_cruzada_con_smote = pd.DataFrame(columns=columnas_resultados)
# Estrategia de validación cruzada
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
# ================================================
# Evaluación con CatBoost
# ================================================
print("\n🚀 Evaluación con CatBoost usando SMOTE en cada fold...")
for fold, (train_idx, test_idx) in enumerate(kfold.split(X, y), 1):
print(f"\n🔄 Fold {fold}:")
# Dividir el fold en entrenamiento y prueba
X_train_fold, X_test_fold = X.iloc[train_idx], X.iloc[test_idx]
y_train_fold, y_test_fold = y.iloc[train_idx], y.iloc[test_idx]
# Aplicar SMOTE en el conjunto de entrenamiento
print("📊 Aplicando SMOTE en el conjunto de entrenamiento...")
smote = SMOTE(sampling_strategy='auto', random_state=42)
X_train_res, y_train_res = smote.fit_resample(X_train_fold, y_train_fold)
# Mostrar distribución después de SMOTE
print("🔍 Distribución DESPUÉS de SMOTE en fold:")
print(y_train_res.value_counts(normalize=True))
# Entrenar y evaluar el modelo
resultados_validacion_cruzada_con_smote = entrenar_y_evaluar(
modelo=CatBoostClassifier,
nombre_modelo="CatBoost",
parametros=parametros_catboost,
X_train=X_train_res,
y_train=y_train_res,
X_test=X_test_fold,
y_test=y_test_fold,
tecnica="Validación Cruzada con SMOTE",
umbral_sobreajuste=10, # Aplicar el umbral ajustado
resultados_df=resultados_validacion_cruzada_con_smote
)
resultados_validacion_cruzada_con_smote['Fold'] = fold # Añadir número de fold
# ================================================
# Evaluación con XGBoost
# ================================================
print("\n🚀 Evaluación con XGBoost usando SMOTE en cada fold...")
for fold, (train_idx, test_idx) in enumerate(kfold.split(X, y), 1):
print(f"\n🔄 Fold {fold}:")
# Dividir el fold en entrenamiento y prueba
X_train_fold, X_test_fold = X.iloc[train_idx], X.iloc[test_idx]
y_train_fold, y_test_fold = y.iloc[train_idx], y.iloc[test_idx]
# Aplicar SMOTE en el conjunto de entrenamiento
print("📊 Aplicando SMOTE en el conjunto de entrenamiento...")
smote = SMOTE(sampling_strategy='auto', random_state=42)
X_train_res, y_train_res = smote.fit_resample(X_train_fold, y_train_fold)
# Mostrar distribución después de SMOTE
print("🔍 Distribución DESPUÉS de SMOTE en fold:")
print(y_train_res.value_counts(normalize=True))
# Entrenar y evaluar el modelo
resultados_validacion_cruzada_con_smote = entrenar_y_evaluar(
modelo=XGBClassifier,
nombre_modelo="XGBoost",
parametros=parametros_xgb,
X_train=X_train_res,
y_train=y_train_res,
X_test=X_test_fold,
y_test=y_test_fold,
tecnica="Validación Cruzada con SMOTE",
umbral_sobreajuste=10, # Aplicar el umbral ajustado
resultados_df=resultados_validacion_cruzada_con_smote
)
resultados_validacion_cruzada_con_smote['Fold'] = fold # Añadir número de fold
# ================================================
# Mostrar y Ordenar Resultados
# ================================================
resultados_ordenados_smote = resultados_validacion_cruzada_con_smote.sort_values(
by=['AUPRC_Test', 'Recall_Test', 'Precision_Test', 'F1-Score_Test'],
ascending=[False, False, False, False]
)
print("\n🏆 Resultados Finales Ordenados (Top Modelos):")
print(resultados_ordenados_smote)
# Guardar los resultados en CSV
output_file = "resultados_validacion_cruzada_smote.csv"
resultados_ordenados_smote.to_csv(output_file, index=False)
print(f"\n✅ Resultados guardados en '{output_file}'")
🚀 Evaluación con CatBoost usando SMOTE en cada fold...
🔄 Fold 1:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6197643 total: 172ms remaining: 51.5s
50: learn: 0.0837850 total: 4.59s remaining: 22.4s
100: learn: 0.0570631 total: 8.8s remaining: 17.3s
150: learn: 0.0452330 total: 15.1s remaining: 14.9s
200: learn: 0.0380934 total: 19.6s remaining: 9.66s
250: learn: 0.0329013 total: 23.8s remaining: 4.65s
299: learn: 0.0291132 total: 29.8s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 93.87
- Recall_Train: 99.97
- AUPRC_Train: 99.80
- Accuracy_Train: 96.72
- F1-Score_Train: 96.82
- Precision_Test: 2.48
- Recall_Test: 94.74
- AUPRC_Test: 67.68
- Accuracy_Test: 93.69
- F1-Score_Test: 4.83
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (1, 135)
🔄 Fold 2:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6198680 total: 86.7ms remaining: 25.9s
50: learn: 0.0873865 total: 4.34s remaining: 21.2s
100: learn: 0.0587008 total: 8.75s remaining: 17.2s
150: learn: 0.0454690 total: 15.2s remaining: 15s
200: learn: 0.0373740 total: 19.4s remaining: 9.55s
250: learn: 0.0314223 total: 23.5s remaining: 4.59s
299: learn: 0.0273196 total: 30s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.32
- Recall_Train: 99.98
- AUPRC_Train: 99.83
- Accuracy_Train: 96.98
- F1-Score_Train: 97.07
- Precision_Test: 2.42
- Recall_Test: 92.11
- AUPRC_Test: 60.34
- Accuracy_Test: 93.71
- F1-Score_Test: 4.72
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (2, 135)
🔄 Fold 3:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6179122 total: 84.5ms remaining: 25.3s
50: learn: 0.0755571 total: 4.35s remaining: 21.2s
100: learn: 0.0504525 total: 10.4s remaining: 20.5s
150: learn: 0.0395327 total: 15.2s remaining: 15s
200: learn: 0.0331644 total: 19.4s remaining: 9.55s
250: learn: 0.0285134 total: 25.3s remaining: 4.94s
299: learn: 0.0250930 total: 30.1s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.50
- Recall_Train: 99.98
- AUPRC_Train: 99.83
- Accuracy_Train: 97.08
- F1-Score_Train: 97.16
- Precision_Test: 2.42
- Recall_Test: 86.84
- AUPRC_Test: 63.91
- Accuracy_Test: 94.07
- F1-Score_Test: 4.72
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (3, 135)
🔄 Fold 4:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6198310 total: 88.8ms remaining: 26.5s
50: learn: 0.0886550 total: 4.38s remaining: 21.4s
100: learn: 0.0602755 total: 11s remaining: 21.7s
150: learn: 0.0481493 total: 15.2s remaining: 15s
200: learn: 0.0405259 total: 19.4s remaining: 9.53s
250: learn: 0.0348938 total: 25.9s remaining: 5.06s
299: learn: 0.0306786 total: 30s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 93.69
- Recall_Train: 99.98
- AUPRC_Train: 99.81
- Accuracy_Train: 96.62
- F1-Score_Train: 96.73
- Precision_Test: 2.36
- Recall_Test: 97.37
- AUPRC_Test: 71.12
- Accuracy_Test: 93.17
- F1-Score_Test: 4.60
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (4, 135)
🔄 Fold 5:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6209804 total: 97.5ms remaining: 29.1s
50: learn: 0.0879092 total: 6.18s remaining: 30.2s
100: learn: 0.0595503 total: 10.9s remaining: 21.4s
150: learn: 0.0480335 total: 15s remaining: 14.8s
200: learn: 0.0405637 total: 20.7s remaining: 10.2s
250: learn: 0.0349176 total: 25.7s remaining: 5.03s
299: learn: 0.0306724 total: 29.9s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 93.68
- Recall_Train: 99.97
- AUPRC_Train: 99.81
- Accuracy_Train: 96.61
- F1-Score_Train: 96.72
- Precision_Test: 2.30
- Recall_Test: 94.74
- AUPRC_Test: 62.59
- Accuracy_Test: 93.20
- F1-Score_Test: 4.50
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (5, 135)
🔄 Fold 6:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6227778 total: 97.2ms remaining: 29.1s
50: learn: 0.0884626 total: 6.61s remaining: 32.3s
100: learn: 0.0578288 total: 10.9s remaining: 21.4s
150: learn: 0.0455781 total: 15.1s remaining: 14.9s
200: learn: 0.0381560 total: 21.8s remaining: 10.7s
250: learn: 0.0323976 total: 25.9s remaining: 5.06s
299: learn: 0.0282838 total: 30s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.12
- Recall_Train: 99.98
- AUPRC_Train: 99.83
- Accuracy_Train: 96.87
- F1-Score_Train: 96.96
- Precision_Test: 2.52
- Recall_Test: 94.74
- AUPRC_Test: 70.72
- Accuracy_Test: 93.80
- F1-Score_Test: 4.91
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (6, 135)
🔄 Fold 7:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6203246 total: 170ms remaining: 50.8s
50: learn: 0.0881837 total: 5.43s remaining: 26.5s
100: learn: 0.0606040 total: 9.64s remaining: 19s
150: learn: 0.0484906 total: 15.1s remaining: 14.9s
200: learn: 0.0410733 total: 22.8s remaining: 11.2s
250: learn: 0.0355084 total: 27s remaining: 5.26s
299: learn: 0.0311114 total: 31.1s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 93.53
- Recall_Train: 99.96
- AUPRC_Train: 99.79
- Accuracy_Train: 96.52
- F1-Score_Train: 96.64
- Precision_Test: 2.31
- Recall_Test: 94.74
- AUPRC_Test: 81.38
- Accuracy_Test: 93.22
- F1-Score_Test: 4.51
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (7, 135)
🔄 Fold 8:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6226187 total: 163ms remaining: 48.9s
50: learn: 0.0854899 total: 5.02s remaining: 24.5s
100: learn: 0.0587468 total: 10s remaining: 19.7s
150: learn: 0.0462800 total: 18.1s remaining: 17.9s
200: learn: 0.0388473 total: 22.3s remaining: 11s
250: learn: 0.0332201 total: 26.4s remaining: 5.16s
299: learn: 0.0292881 total: 33s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.15
- Recall_Train: 99.97
- AUPRC_Train: 99.83
- Accuracy_Train: 96.88
- F1-Score_Train: 96.97
- Precision_Test: 2.52
- Recall_Test: 94.74
- AUPRC_Test: 70.05
- Accuracy_Test: 93.80
- F1-Score_Test: 4.91
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (8, 135)
🔄 Fold 9:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6205673 total: 85.1ms remaining: 25.5s
50: learn: 0.0895558 total: 4.32s remaining: 21.1s
100: learn: 0.0607633 total: 9.37s remaining: 18.5s
150: learn: 0.0478809 total: 15s remaining: 14.8s
200: learn: 0.0404560 total: 19.1s remaining: 9.43s
250: learn: 0.0347237 total: 23.9s remaining: 4.66s
299: learn: 0.0307483 total: 29.8s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 93.68
- Recall_Train: 99.96
- AUPRC_Train: 99.78
- Accuracy_Train: 96.60
- F1-Score_Train: 96.71
- Precision_Test: 2.23
- Recall_Test: 97.30
- AUPRC_Test: 78.07
- Accuracy_Test: 92.96
- F1-Score_Test: 4.36
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (9, 135)
🔄 Fold 10:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando CatBoost (Validación Cruzada con SMOTE)...
0: learn: 0.6207747 total: 83.5ms remaining: 25s
50: learn: 0.0776857 total: 4.25s remaining: 20.8s
100: learn: 0.0530933 total: 10.7s remaining: 21.2s
150: learn: 0.0419197 total: 14.9s remaining: 14.7s
200: learn: 0.0347746 total: 19.1s remaining: 9.4s
250: learn: 0.0297048 total: 25.5s remaining: 4.98s
299: learn: 0.0261034 total: 29.8s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada con SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.72
- Recall_Train: 99.90
- AUPRC_Train: 99.81
- Accuracy_Train: 97.17
- F1-Score_Train: 97.24
- Precision_Test: 2.34
- Recall_Test: 83.78
- AUPRC_Test: 54.96
- Accuracy_Test: 94.23
- F1-Score_Test: 4.56
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 3
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 15.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (10, 135)
🚀 Evaluación con XGBoost usando SMOTE en cada fold...
🔄 Fold 1:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.98
- Recall_Train: 99.92
- AUPRC_Train: 99.89
- Accuracy_Train: 97.32
- F1-Score_Train: 97.39
- Precision_Test: 3.13
- Recall_Test: 94.74
- AUPRC_Test: 77.93
- Accuracy_Test: 95.04
- F1-Score_Test: 6.07
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (11, 136)
🔄 Fold 2:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 95.23
- Recall_Train: 99.99
- AUPRC_Train: 99.91
- Accuracy_Train: 97.49
- F1-Score_Train: 97.55
- Precision_Test: 2.76
- Recall_Test: 89.47
- AUPRC_Test: 67.44
- Accuracy_Test: 94.66
- F1-Score_Test: 5.36
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (12, 136)
🔄 Fold 3:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 95.23
- Recall_Train: 99.99
- AUPRC_Train: 99.91
- Accuracy_Train: 97.49
- F1-Score_Train: 97.55
- Precision_Test: 2.93
- Recall_Test: 89.47
- AUPRC_Test: 71.50
- Accuracy_Test: 94.97
- F1-Score_Test: 5.67
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (13, 136)
🔄 Fold 4:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.49
- Recall_Train: 99.94
- AUPRC_Train: 99.89
- Accuracy_Train: 97.05
- F1-Score_Train: 97.14
- Precision_Test: 2.68
- Recall_Test: 97.37
- AUPRC_Test: 77.59
- Accuracy_Test: 94.02
- F1-Score_Test: 5.22
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (14, 136)
🔄 Fold 5:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.92
- Recall_Train: 99.98
- AUPRC_Train: 99.89
- Accuracy_Train: 97.31
- F1-Score_Train: 97.38
- Precision_Test: 2.84
- Recall_Test: 94.74
- AUPRC_Test: 61.09
- Accuracy_Test: 94.52
- F1-Score_Test: 5.52
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (15, 136)
🔄 Fold 6:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.89
- Recall_Train: 99.91
- AUPRC_Train: 99.91
- Accuracy_Train: 97.27
- F1-Score_Train: 97.34
- Precision_Test: 2.91
- Recall_Test: 94.74
- AUPRC_Test: 78.19
- Accuracy_Test: 94.64
- F1-Score_Test: 5.64
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (16, 136)
🔄 Fold 7:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.76
- Recall_Train: 99.98
- AUPRC_Train: 99.88
- Accuracy_Train: 97.23
- F1-Score_Train: 97.30
- Precision_Test: 2.85
- Recall_Test: 94.74
- AUPRC_Test: 88.19
- Accuracy_Test: 94.53
- F1-Score_Test: 5.53
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (17, 136)
🔄 Fold 8:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 95.11
- Recall_Train: 99.94
- AUPRC_Train: 99.89
- Accuracy_Train: 97.40
- F1-Score_Train: 97.47
- Precision_Test: 3.05
- Recall_Test: 94.74
- AUPRC_Test: 66.82
- Accuracy_Test: 94.90
- F1-Score_Test: 5.91
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (18, 136)
🔄 Fold 9:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 94.59
- Recall_Train: 99.94
- AUPRC_Train: 99.87
- Accuracy_Train: 97.11
- F1-Score_Train: 97.19
- Precision_Test: 2.72
- Recall_Test: 100.00
- AUPRC_Test: 84.40
- Accuracy_Test: 94.10
- F1-Score_Test: 5.29
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (19, 136)
🔄 Fold 10:
📊 Aplicando SMOTE en el conjunto de entrenamiento...
🔍 Distribución DESPUÉS de SMOTE en fold:
Class
0 0.50000000
1 0.50000000
Name: proportion, dtype: float64
🚀 Entrenando XGBoost (Validación Cruzada con SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada con SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada con SMOTE
- Sobreajuste: 1
- Precision_Train: 95.49
- Recall_Train: 99.92
- AUPRC_Train: 99.91
- Accuracy_Train: 97.60
- F1-Score_Train: 97.65
- Precision_Test: 2.82
- Recall_Test: 83.78
- AUPRC_Test: 65.16
- Accuracy_Test: 95.21
- F1-Score_Test: 5.45
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (20, 136)
🏆 Resultados Finales Ordenados (Top Modelos):
Modelo Tecnica Sobreajuste Precision_Train \
16 XGBoost Validación Cruzada con SMOTE 1 94.76261114
18 XGBoost Validación Cruzada con SMOTE 1 94.59012244
6 CatBoost Validación Cruzada con SMOTE 1 93.52864034
15 XGBoost Validación Cruzada con SMOTE 1 94.89387924
8 CatBoost Validación Cruzada con SMOTE 1 93.67709653
10 XGBoost Validación Cruzada con SMOTE 1 94.97641443
13 XGBoost Validación Cruzada con SMOTE 1 94.48622375
12 XGBoost Validación Cruzada con SMOTE 1 95.22670780
3 CatBoost Validación Cruzada con SMOTE 1 93.68708317
5 CatBoost Validación Cruzada con SMOTE 1 94.11679690
7 CatBoost Validación Cruzada con SMOTE 1 94.14895354
0 CatBoost Validación Cruzada con SMOTE 1 93.86611482
11 XGBoost Validación Cruzada con SMOTE 1 95.22677422
17 XGBoost Validación Cruzada con SMOTE 1 95.11092677
19 XGBoost Validación Cruzada con SMOTE 1 95.48541599
2 CatBoost Validación Cruzada con SMOTE 1 94.49939844
4 CatBoost Validación Cruzada con SMOTE 1 93.67646718
14 XGBoost Validación Cruzada con SMOTE 1 94.91566866
1 CatBoost Validación Cruzada con SMOTE 1 94.32083653
9 CatBoost Validación Cruzada con SMOTE 1 94.72352016
Recall_Train AUPRC_Train Accuracy_Train F1-Score_Train Precision_Test \
16 99.98167464 99.88237549 97.22791794 97.30220855 2.85035629
18 99.94304280 99.86959059 97.11350827 97.19293514 2.71659325
6 99.96334928 99.79403119 96.52338217 96.63899948 2.31065469
15 99.91431656 99.90857611 97.26902618 97.33940660 2.90791599
8 99.95592008 99.77748689 96.60460808 96.71470845 2.22772277
10 99.92125048 99.88541548 97.31805890 97.38610363 3.13315927
13 99.93809000 99.89404267 97.05308411 97.13571879 2.68310370
12 99.99356136 99.90907674 97.49066397 97.55193649 2.92850991
3 99.97771240 99.80735199 96.62045704 96.73023152 2.35668790
5 99.98365576 99.82986452 96.86685884 96.96156081 2.52277505
7 99.96978792 99.82811916 96.87849791 96.97209911 2.52100840
0 99.96978792 99.79652302 96.71852248 96.82185249 2.47933884
11 99.98514160 99.90586186 97.48670173 97.54796437 2.76198213
17 99.94452864 99.89164278 97.40349470 97.46783780 3.05084746
19 99.92471744 99.90529428 97.60012085 97.65464098 2.81562216
2 99.97870296 99.82816188 97.07958159 97.16186263 2.42468773
4 99.96731152 99.80694122 96.60956088 96.71970501 2.30473752
14 99.97721712 99.88944374 97.31087734 97.38071634 2.84360190
1 99.98365576 99.82593432 96.98176379 97.06972741 2.42047026
9 99.90242984 99.80877857 97.16873198 97.24407076 2.34493192
Recall_Test AUPRC_Test Accuracy_Test F1-Score_Test iterations \
16 94.73684211 88.19208953 94.53097188 5.53420446 NaN
18 100.00000000 84.40388489 94.10351119 5.28949249 NaN
6 94.73684211 81.38025890 93.21822713 4.51127820 300
15 94.73684211 78.18604093 94.64222143 5.64263323 NaN
8 97.29729730 78.06690064 92.96426505 4.35571688 300
10 94.73684211 77.92951787 95.03826985 6.06571188 NaN
13 97.36842105 77.59308652 94.02367391 5.22230064 NaN
12 89.47368421 71.49855691 94.96707013 5.67139283 NaN
3 97.36842105 71.12010920 93.17372731 4.60199005 300
5 94.73684211 70.72422839 93.80117480 4.91467577 300
7 94.73684211 70.04893483 93.79672481 4.91132333 300
0 94.73684211 67.68355898 93.68992524 4.83221477 300
11 89.47368421 67.44480844 94.65557138 5.35855004 NaN
17 94.73684211 66.82054784 94.90032040 5.91133005 NaN
19 83.78378378 65.15597638 95.21160607 5.44815466 NaN
2 86.84210526 63.90756485 94.06817373 4.71765547 300
4 94.73684211 62.58593364 93.20042720 4.50000000 300
14 94.73684211 61.08541061 94.51762193 5.52147239 NaN
1 92.10526316 60.34089979 93.70772517 4.71698113 300
9 83.78378378 54.96252475 94.22811624 4.56217807 300
learning_rate depth class_weights verbose max_depth n_estimators \
16 NaN NaN NaN NaN None None
18 NaN NaN NaN NaN None None
6 0.03000000 3 [1, 10] 50 None None
15 NaN NaN NaN NaN None None
8 0.03000000 3 [1, 10] 50 None None
10 NaN NaN NaN NaN None None
13 NaN NaN NaN NaN None None
12 NaN NaN NaN NaN None None
3 0.03000000 3 [1, 10] 50 None None
5 0.03000000 3 [1, 10] 50 None None
7 0.03000000 3 [1, 10] 50 None None
0 0.03000000 3 [1, 10] 50 None None
11 NaN NaN NaN NaN None None
17 NaN NaN NaN NaN None None
19 NaN NaN NaN NaN NaN NaN
2 0.03000000 3 [1, 10] 50 None None
4 0.03000000 3 [1, 10] 50 None None
14 NaN NaN NaN NaN None None
1 0.03000000 3 [1, 10] 50 None None
9 0.03000000 3 [1, 10] 50 None None
scale_pos_weight min_child_weight gamma l2_leaf_reg subsample Fold \
16 None NaN NaN NaN None 10
18 None NaN NaN NaN None 10
6 None NaN NaN 15.00000000 None 10
15 None NaN NaN NaN None 10
8 None NaN NaN 15.00000000 None 10
10 None NaN NaN NaN None 10
13 None NaN NaN NaN None 10
12 None NaN NaN NaN None 10
3 None NaN NaN 15.00000000 None 10
5 None NaN NaN 15.00000000 None 10
7 None NaN NaN 15.00000000 None 10
0 None NaN NaN 15.00000000 None 10
11 None NaN NaN NaN None 10
17 None NaN NaN NaN None 10
19 NaN NaN NaN NaN NaN 10
2 None NaN NaN 15.00000000 None 10
4 None NaN NaN 15.00000000 None 10
14 None NaN NaN NaN None 10
1 None NaN NaN 15.00000000 None 10
9 None NaN NaN 15.00000000 None 10
model_shrink_mode per_feature_ctr eta devices \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
per_object_feature_penalties allow_const_label mvs_reg \
16 None None None
18 None None None
6 None None None
15 None None None
8 None None None
10 None None None
13 None None None
12 None None None
3 None None None
5 None None None
7 None None None
0 None None None
11 None None None
17 None None None
19 NaN NaN NaN
2 None None None
4 None None None
14 None None None
1 None None None
9 None None None
dev_score_calc_obj_block_size ctr_leaf_count_limit max_ctr_complexity \
16 None None None
18 None None None
6 None None None
15 None None None
8 None None None
10 None None None
13 None None None
12 None None None
3 None None None
5 None None None
7 None None None
0 None None None
11 None None None
17 None None None
19 NaN NaN NaN
2 None None None
4 None None None
14 None None None
1 None None None
9 None None None
target_border metric_period eval_fraction allow_writing_files \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
save_snapshot classes_count ctr_description leaf_estimation_method \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
one_hot_max_size min_data_in_leaf random_score_type colsample_bylevel \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
bootstrap_type custom_metric thread_count bagging_temperature \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
random_strength nan_mode text_features per_float_feature_quantization \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
simple_ctr output_borders use_best_model gpu_cat_features_storage \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
combinations_ctr border_count feature_border_type data_partition \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
fold_permutation_block od_pval name early_stopping_rounds tokenizers \
16 None None None None None
18 None None None None None
6 None None None None None
15 None None None None None
8 None None None None None
10 None None None None None
13 None None None None None
12 None None None None None
3 None None None None None
5 None None None None None
7 None None None None None
0 None None None None None
11 None None None None None
17 None None None None None
19 NaN NaN NaN NaN NaN
2 None None None None None
4 None None None None None
14 None None None None None
1 None None None None None
9 None None None None None
best_model_min_trees dev_efb_max_buckets feature_weights \
16 None None None
18 None None None
6 None None None
15 None None None
8 None None None
10 None None None
13 None None None
12 None None None
3 None None None
5 None None None
7 None None None
0 None None None
11 None None None
17 None None None
19 NaN NaN NaN
2 None None None
4 None None None
14 None None None
1 None None None
9 None None None
posterior_sampling metadata boosting_type diffusion_temperature \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
gpu_ram_part score_function approx_on_full_history sampling_unit task_type \
16 None None None None None
18 None None None None None
6 None None None None None
15 None None None None None
8 None None None None None
10 None None None None None
13 None None None None None
12 None None None None None
3 None None None None None
5 None None None None None
7 None None None None None
0 None None None None None
11 None None None None None
17 None None None None None
19 NaN NaN NaN NaN NaN
2 None None None None None
4 None None None None None
14 None None None None None
1 None None None None None
9 None None None None None
snapshot_interval rsm store_all_simple_ctr random_seed \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
sampling_frequency ctr_target_border_count final_ctr_computation_mode \
16 None None None
18 None None None
6 None None None
15 None None None
8 None None None
10 None None None
13 None None None
12 None None None
3 None None None
5 None None None
7 None None None
0 None None None
11 None None None
17 None None None
19 NaN NaN NaN
2 None None None
4 None None None
14 None None None
1 None None None
9 None None None
fixed_binary_splits auto_class_weights ctr_history_unit device_config \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
leaf_estimation_backtracking has_time fold_len_multiplier \
16 None None None
18 None None None
6 None None None
15 None None None
8 None None None
10 None None None
13 None None None
12 None None None
3 None None None
5 None None None
7 None None None
0 None None None
11 None None None
17 None None None
19 NaN NaN NaN
2 None None None
4 None None None
14 None None None
1 None None None
9 None None None
pinned_memory_size feature_calcers model_shrink_rate od_type \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
monotone_constraints dictionaries max_bin boost_from_average grow_policy \
16 None None None None None
18 None None None None None
6 None None None None None
15 None None None None None
8 None None None None None
10 None None None None None
13 None None None None None
12 None None None None None
3 None None None None None
5 None None None None None
7 None None None None None
0 None None None None None
11 None None None None None
17 None None None None None
19 NaN NaN NaN NaN NaN
2 None None None None None
4 None None None None None
14 None None None None None
1 None None None None None
9 None None None None None
embedding_features langevin callback cat_features train_dir \
16 None None None None None
18 None None None None None
6 None None None None None
15 None None None None None
8 None None None None None
10 None None None None None
13 None None None None None
12 None None None None None
3 None None None None None
5 None None None None None
7 None None None None None
0 None None None None None
11 None None None None None
17 None None None None None
19 NaN NaN NaN NaN NaN
2 None None None None None
4 None None None None None
14 None None None None None
1 None None None None None
9 None None None None None
sparse_features_conflict_fraction ignored_features num_trees \
16 None None None
18 None None None
6 None None None
15 None None None
8 None None None
10 None None None
13 None None None
12 None None None
3 None None None
5 None None None
7 None None None
0 None None None
11 None None None
17 None None None
19 NaN NaN NaN
2 None None None
4 None None None
14 None None None
1 None None None
9 None None None
penalties_coefficient objective used_ram_limit text_processing reg_lambda \
16 None None None None None
18 None None None None None
6 None None None None None
15 None None None None None
8 None None None None None
10 None None None None None
13 None None None None None
12 None None None None None
3 None None None None None
5 None None None None None
7 None None None None None
0 None None None None None
11 None None None None None
17 None None None None None
19 NaN None NaN NaN NaN
2 None None None None None
4 None None None None None
14 None None None None None
1 None None None None None
9 None None None None None
snapshot_file random_state custom_loss loss_function \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
leaf_estimation_iterations silent max_leaves input_borders \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
counter_calc_method num_boost_round model_size_reg eval_metric num_leaves \
16 None None None None None
18 None None None None None
6 None None None None None
15 None None None None None
8 None None None None None
10 None None None None None
13 None None None None None
12 None None None None None
3 None None None None None
5 None None None None None
7 None None None None None
0 None None None None None
11 None None None None None
17 None None None None None
19 NaN NaN NaN NaN NaN
2 None None None None None
4 None None None None None
14 None None None None None
1 None None None None None
9 None None None None None
min_child_samples class_names logging_level first_feature_use_penalties \
16 None None None None
18 None None None None
6 None None None None
15 None None None None
8 None None None None
10 None None None None
13 None None None None
12 None None None None
3 None None None None
5 None None None None
7 None None None None
0 None None None None
11 None None None None
17 None None None None
19 NaN NaN NaN NaN
2 None None None None
4 None None None None
14 None None None None
1 None None None None
9 None None None None
od_wait kwargs
16 None NaN
18 None NaN
6 None NaN
15 None NaN
8 None NaN
10 None NaN
13 None NaN
12 None NaN
3 None NaN
5 None NaN
7 None NaN
0 None NaN
11 None NaN
17 None NaN
19 NaN None
2 None NaN
4 None NaN
14 None NaN
1 None NaN
9 None NaN
✅ Resultados guardados en 'resultados_validacion_cruzada_smote.csv'
CPU times: user 13min 59s, sys: 10.7 s, total: 14min 10s
Wall time: 8min 57s
3. Validación Cruzada con cross_val_score (SIN SMOTE)¶
La validación cruzada permite evaluar el modelo en múltiples subconjuntos del dataset para reducir la varianza de los resultados y evitar sobreajuste.
SMOTE genera puntos sintéticos que pueden facilitar que el modelo memorice patrones artificiales en lugar de generalizar, por esto probamos la validación cruzada sobre en dataset original (clean_data).
Evaluar el modelo sin SMOTE permite analizar cómo se desempeña con los datos originales.
In [ ]:
# ==========================================================
# 3. Validación Cruzada SIN SMOTE
# ==========================================================
%%time
from sklearn.model_selection import StratifiedKFold
from catboost import CatBoostClassifier
from xgboost import XGBClassifier
import pandas as pd
# Configuraciones de hiperparámetros
parametros_catboost = {
'iterations': 300,
'learning_rate': 0.03,
'depth': 4,
'l2_leaf_reg': 10.0, # Mayor regularización
'class_weights': [1, 10], # Balanceo de clases nativo
'verbose': 50
}
parametros_xgb = {
'learning_rate': 0.03,
'max_depth': 4,
'n_estimators': 300,
'min_child_weight': 2, # Evita árboles muy profundos
'scale_pos_weight': 10, # Balanceo de clases
'gamma': 0.2, # Regularización adicional
'subsample': 0.8 # Submuestra del 80%
}
# Datos originales
X = X_train # Usar datos originales de entrenamiento
y = y_train # Etiquetas de entrenamiento
# Estrategia de validación cruzada
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
# Lista completa de columnas esperadas en resultados_maestro
columnas_resultados = [
'Modelo', 'Tecnica', 'Sobreajuste',
'Precision_Train', 'Recall_Train', 'AUPRC_Train', 'Accuracy_Train', 'F1-Score_Train',
'Precision_Test', 'Recall_Test', 'AUPRC_Test', 'Accuracy_Test', 'F1-Score_Test',
'iterations', 'learning_rate', 'depth', 'class_weights', 'verbose',
'max_depth', 'n_estimators', 'scale_pos_weight', 'min_child_weight',
'gamma', 'l2_leaf_reg', 'subsample', 'Fold'
]
# Inicializar DataFrame para almacenar resultados
resultados_validacion_cruzada_sin_smote = pd.DataFrame(columns=columnas_resultados)
# ================================================
# Evaluación con CatBoost SIN SMOTE
# ================================================
print("\n🚀 Evaluación con CatBoost SIN SMOTE en cada fold...")
for fold, (train_idx, test_idx) in enumerate(kfold.split(X, y), 1):
print(f"\n🔎 Fold {fold}/{kfold.get_n_splits()} - Entrenando CatBoost...")
# Dividir el fold en entrenamiento y prueba
X_train_fold, X_test_fold = X.iloc[train_idx], X.iloc[test_idx]
y_train_fold, y_test_fold = y.iloc[train_idx], y.iloc[test_idx]
# Entrenar y evaluar el modelo
resultados_validacion_cruzada_sin_smote = entrenar_y_evaluar(
modelo=CatBoostClassifier,
nombre_modelo="CatBoost",
parametros=parametros_catboost,
X_train=X_train_fold,
y_train=y_train_fold,
X_test=X_test_fold,
y_test=y_test_fold,
tecnica="Validación Cruzada SIN SMOTE",
umbral_sobreajuste=10, # Aplicar umbral ajustado
resultados_df=resultados_validacion_cruzada_sin_smote
)
resultados_validacion_cruzada_sin_smote.loc[resultados_validacion_cruzada_sin_smote.index[-1], 'Fold'] = fold
# ================================================
# Evaluación con XGBoost SIN SMOTE
# ================================================
print("\n🚀 Evaluación con XGBoost SIN SMOTE en cada fold...")
for fold, (train_idx, test_idx) in enumerate(kfold.split(X, y), 1):
print(f"\n🔎 Fold {fold}/{kfold.get_n_splits()} - Entrenando XGBoost...")
# Dividir el fold en entrenamiento y prueba
X_train_fold, X_test_fold = X.iloc[train_idx], X.iloc[test_idx]
y_train_fold, y_test_fold = y.iloc[train_idx], y.iloc[test_idx]
# Entrenar y evaluar el modelo
resultados_validacion_cruzada_sin_smote = entrenar_y_evaluar(
modelo=XGBClassifier,
nombre_modelo="XGBoost",
parametros=parametros_xgb,
X_train=X_train_fold,
y_train=y_train_fold,
X_test=X_test_fold,
y_test=y_test_fold,
tecnica="Validación Cruzada SIN SMOTE",
umbral_sobreajuste=10, # Aplicar umbral ajustado
resultados_df=resultados_validacion_cruzada_sin_smote
)
resultados_validacion_cruzada_sin_smote.loc[resultados_validacion_cruzada_sin_smote.index[-1], 'Fold'] = fold
# ================================================
# Mostrar y Ordenar Resultados
# ================================================
resultados_ordenados_sin_smote = resultados_validacion_cruzada_sin_smote.sort_values(
by=['AUPRC_Test', 'Recall_Test', 'Precision_Test', 'F1-Score_Test'],
ascending=[False, False, False, False]
)
print("\n🏆 Resultados Finales Ordenados (Top Modelos):")
print(resultados_ordenados_sin_smote)
# Guardar los resultados en CSV
output_file = "resultados_validacion_cruzada_sin_smote.csv"
resultados_ordenados_sin_smote.to_csv(output_file, index=False)
print(f"\n✅ Resultados guardados en '{output_file}'")
🚀 Evaluación con CatBoost SIN SMOTE en cada fold...
🔎 Fold 1/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6054052 total: 46.6ms remaining: 13.9s
50: learn: 0.0203590 total: 2.49s remaining: 12.2s
100: learn: 0.0149727 total: 4.89s remaining: 9.63s
150: learn: 0.0131578 total: 7.27s remaining: 7.17s
200: learn: 0.0119017 total: 11.6s remaining: 5.73s
250: learn: 0.0109190 total: 14.9s remaining: 2.9s
299: learn: 0.0099803 total: 17.2s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 82.02
- Recall_Train: 88.53
- AUPRC_Train: 90.30
- Accuracy_Train: 99.95
- F1-Score_Train: 85.15
- Precision_Test: 79.49
- Recall_Test: 81.58
- AUPRC_Test: 86.04
- Accuracy_Test: 99.93
- F1-Score_Test: 80.52
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (1, 135)
🔎 Fold 2/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6041546 total: 46.2ms remaining: 13.8s
50: learn: 0.0199121 total: 2.45s remaining: 11.9s
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299: learn: 0.0096953 total: 17.1s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 84.31
- Recall_Train: 88.53
- AUPRC_Train: 90.45
- Accuracy_Train: 99.95
- F1-Score_Train: 86.37
- Precision_Test: 83.33
- Recall_Test: 78.95
- AUPRC_Test: 81.39
- Accuracy_Test: 99.94
- F1-Score_Test: 81.08
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (2, 135)
🔎 Fold 3/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6024564 total: 46.9ms remaining: 14s
50: learn: 0.0196367 total: 3.01s remaining: 14.7s
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250: learn: 0.0099498 total: 14.1s remaining: 2.75s
299: learn: 0.0090632 total: 16.3s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 82.34
- Recall_Train: 89.12
- AUPRC_Train: 90.97
- Accuracy_Train: 99.95
- F1-Score_Train: 85.59
- Precision_Test: 86.49
- Recall_Test: 84.21
- AUPRC_Test: 82.43
- Accuracy_Test: 99.95
- F1-Score_Test: 85.33
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (3, 135)
🔎 Fold 4/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6034793 total: 103ms remaining: 30.7s
50: learn: 0.0202511 total: 4.66s remaining: 22.8s
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250: learn: 0.0109478 total: 13.7s remaining: 2.67s
299: learn: 0.0100997 total: 17.3s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 82.47
- Recall_Train: 88.53
- AUPRC_Train: 90.10
- Accuracy_Train: 99.95
- F1-Score_Train: 85.39
- Precision_Test: 83.33
- Recall_Test: 78.95
- AUPRC_Test: 82.11
- Accuracy_Test: 99.94
- F1-Score_Test: 81.08
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (4, 135)
🔎 Fold 5/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6053334 total: 45.1ms remaining: 13.5s
50: learn: 0.0201140 total: 2.35s remaining: 11.5s
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250: learn: 0.0107222 total: 12.9s remaining: 2.52s
299: learn: 0.0098954 total: 16.1s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 84.87
- Recall_Train: 89.12
- AUPRC_Train: 90.34
- Accuracy_Train: 99.96
- F1-Score_Train: 86.94
- Precision_Test: 65.96
- Recall_Test: 81.58
- AUPRC_Test: 77.20
- Accuracy_Test: 99.90
- F1-Score_Test: 72.94
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (5, 135)
🔎 Fold 6/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6047216 total: 46.6ms remaining: 13.9s
50: learn: 0.0205041 total: 2.35s remaining: 11.5s
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250: learn: 0.0111330 total: 14.1s remaining: 2.76s
299: learn: 0.0102100 total: 16.4s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 81.92
- Recall_Train: 87.94
- AUPRC_Train: 89.82
- Accuracy_Train: 99.95
- F1-Score_Train: 84.82
- Precision_Test: 80.49
- Recall_Test: 86.84
- AUPRC_Test: 87.71
- Accuracy_Test: 99.94
- F1-Score_Test: 83.54
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (6, 135)
🔎 Fold 7/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6044617 total: 44.2ms remaining: 13.2s
50: learn: 0.0201969 total: 2.33s remaining: 11.4s
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250: learn: 0.0110760 total: 14.2s remaining: 2.76s
299: learn: 0.0102029 total: 16.4s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 83.15
- Recall_Train: 88.53
- AUPRC_Train: 89.64
- Accuracy_Train: 99.95
- F1-Score_Train: 85.75
- Precision_Test: 94.29
- Recall_Test: 86.84
- AUPRC_Test: 87.96
- Accuracy_Test: 99.97
- F1-Score_Test: 90.41
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (7, 135)
🔎 Fold 8/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6048882 total: 47.7ms remaining: 14.3s
50: learn: 0.0199723 total: 2.56s remaining: 12.5s
100: learn: 0.0147744 total: 7.2s remaining: 14.2s
150: learn: 0.0131513 total: 9.51s remaining: 9.39s
200: learn: 0.0119062 total: 11.8s remaining: 5.82s
250: learn: 0.0109846 total: 14.1s remaining: 2.76s
299: learn: 0.0100997 total: 16.4s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 82.92
- Recall_Train: 88.53
- AUPRC_Train: 89.94
- Accuracy_Train: 99.95
- F1-Score_Train: 85.63
- Precision_Test: 78.05
- Recall_Test: 84.21
- AUPRC_Test: 83.72
- Accuracy_Test: 99.93
- F1-Score_Test: 81.01
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (8, 135)
🔎 Fold 9/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6056109 total: 100ms remaining: 30s
50: learn: 0.0207826 total: 4.72s remaining: 23s
100: learn: 0.0156416 total: 7.01s remaining: 13.8s
150: learn: 0.0137892 total: 9.28s remaining: 9.15s
200: learn: 0.0125089 total: 11.6s remaining: 5.71s
250: learn: 0.0114481 total: 13.9s remaining: 2.71s
299: learn: 0.0106068 total: 17.6s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 82.27
- Recall_Train: 87.10
- AUPRC_Train: 89.23
- Accuracy_Train: 99.95
- F1-Score_Train: 84.62
- Precision_Test: 91.67
- Recall_Test: 89.19
- AUPRC_Test: 92.67
- Accuracy_Test: 99.97
- F1-Score_Test: 90.41
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (9, 135)
🔎 Fold 10/10 - Entrenando CatBoost...
🚀 Entrenando CatBoost (Validación Cruzada SIN SMOTE)...
0: learn: 0.6044235 total: 97.5ms remaining: 29.2s
50: learn: 0.0196157 total: 2.44s remaining: 11.9s
100: learn: 0.0140260 total: 4.73s remaining: 9.31s
150: learn: 0.0123771 total: 7.01s remaining: 6.92s
200: learn: 0.0111960 total: 9.31s remaining: 4.59s
250: learn: 0.0102205 total: 13s remaining: 2.54s
299: learn: 0.0093952 total: 16.5s remaining: 0us
✅ Resultados para CatBoost (Validación Cruzada SIN SMOTE):
- Modelo: CatBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 83.66
- Recall_Train: 88.56
- AUPRC_Train: 91.11
- Accuracy_Train: 99.95
- F1-Score_Train: 86.04
- Precision_Test: 73.17
- Recall_Test: 81.08
- AUPRC_Test: 76.25
- Accuracy_Test: 99.92
- F1-Score_Test: 76.92
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: 50
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 300
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.03
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: 10.00
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 10]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (10, 135)
🚀 Evaluación con XGBoost SIN SMOTE en cada fold...
🔎 Fold 1/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 90.38
- Recall_Train: 96.76
- AUPRC_Train: 98.48
- Accuracy_Train: 99.98
- F1-Score_Train: 93.47
- Precision_Test: 86.49
- Recall_Test: 84.21
- AUPRC_Test: 87.94
- Accuracy_Test: 99.95
- F1-Score_Test: 85.33
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (11, 136)
🔎 Fold 2/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 91.67
- Recall_Train: 97.06
- AUPRC_Train: 98.73
- Accuracy_Train: 99.98
- F1-Score_Train: 94.29
- Precision_Test: 90.62
- Recall_Test: 76.32
- AUPRC_Test: 81.86
- Accuracy_Test: 99.95
- F1-Score_Test: 82.86
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (12, 136)
🔎 Fold 3/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 88.53
- Recall_Train: 97.65
- AUPRC_Train: 98.13
- Accuracy_Train: 99.97
- F1-Score_Train: 92.87
- Precision_Test: 91.43
- Recall_Test: 84.21
- AUPRC_Test: 83.62
- Accuracy_Test: 99.96
- F1-Score_Test: 87.67
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (13, 136)
🔎 Fold 4/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 91.11
- Recall_Train: 96.47
- AUPRC_Train: 98.31
- Accuracy_Train: 99.98
- F1-Score_Train: 93.71
- Precision_Test: 87.10
- Recall_Test: 71.05
- AUPRC_Test: 83.01
- Accuracy_Test: 99.93
- F1-Score_Test: 78.26
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (14, 136)
🔎 Fold 5/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 91.69
- Recall_Train: 97.35
- AUPRC_Train: 98.74
- Accuracy_Train: 99.98
- F1-Score_Train: 94.44
- Precision_Test: 75.61
- Recall_Test: 81.58
- AUPRC_Test: 79.57
- Accuracy_Test: 99.92
- F1-Score_Test: 78.48
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (15, 136)
🔎 Fold 6/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 91.36
- Recall_Train: 96.47
- AUPRC_Train: 98.40
- Accuracy_Train: 99.98
- F1-Score_Train: 93.85
- Precision_Test: 84.62
- Recall_Test: 86.84
- AUPRC_Test: 89.80
- Accuracy_Test: 99.95
- F1-Score_Test: 85.71
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (16, 136)
🔎 Fold 7/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 90.33
- Recall_Train: 96.18
- AUPRC_Train: 98.26
- Accuracy_Train: 99.98
- F1-Score_Train: 93.16
- Precision_Test: 96.97
- Recall_Test: 84.21
- AUPRC_Test: 87.49
- Accuracy_Test: 99.97
- F1-Score_Test: 90.14
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (17, 136)
🔎 Fold 8/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 91.41
- Recall_Train: 97.06
- AUPRC_Train: 98.35
- Accuracy_Train: 99.98
- F1-Score_Train: 94.15
- Precision_Test: 83.78
- Recall_Test: 81.58
- AUPRC_Test: 85.79
- Accuracy_Test: 99.94
- F1-Score_Test: 82.67
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (18, 136)
🔎 Fold 9/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 0
- Precision_Train: 89.84
- Recall_Train: 95.89
- AUPRC_Train: 98.10
- Accuracy_Train: 99.97
- F1-Score_Train: 92.77
- Precision_Test: 94.29
- Recall_Test: 89.19
- AUPRC_Test: 95.20
- Accuracy_Test: 99.97
- F1-Score_Test: 91.67
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (19, 136)
🔎 Fold 10/10 - Entrenando XGBoost...
🚀 Entrenando XGBoost (Validación Cruzada SIN SMOTE)...
✅ Resultados para XGBoost (Validación Cruzada SIN SMOTE):
- Modelo: XGBoost
- Tecnica: Validación Cruzada SIN SMOTE
- Sobreajuste: 1
- Precision_Train: 92.18
- Recall_Train: 96.77
- AUPRC_Train: 98.74
- Accuracy_Train: 99.98
- F1-Score_Train: 94.42
- Precision_Test: 81.08
- Recall_Test: 81.08
- AUPRC_Test: 75.75
- Accuracy_Test: 99.94
- F1-Score_Test: 81.08
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (20, 136)
🏆 Resultados Finales Ordenados (Top Modelos):
Modelo Tecnica Sobreajuste Precision_Train \
18 XGBoost Validación Cruzada SIN SMOTE 0 89.83516484
8 CatBoost Validación Cruzada SIN SMOTE 0 82.27146814
15 XGBoost Validación Cruzada SIN SMOTE 0 91.36490251
6 CatBoost Validación Cruzada SIN SMOTE 1 83.14917127
10 XGBoost Validación Cruzada SIN SMOTE 1 90.38461538
5 CatBoost Validación Cruzada SIN SMOTE 0 81.91780822
16 XGBoost Validación Cruzada SIN SMOTE 1 90.33149171
0 CatBoost Validación Cruzada SIN SMOTE 0 82.01634877
17 XGBoost Validación Cruzada SIN SMOTE 1 91.41274238
7 CatBoost Validación Cruzada SIN SMOTE 0 82.92011019
12 XGBoost Validación Cruzada SIN SMOTE 1 88.53333333
13 XGBoost Validación Cruzada SIN SMOTE 1 91.11111111
2 CatBoost Validación Cruzada SIN SMOTE 0 82.33695652
3 CatBoost Validación Cruzada SIN SMOTE 0 82.46575342
11 XGBoost Validación Cruzada SIN SMOTE 1 91.66666667
1 CatBoost Validación Cruzada SIN SMOTE 0 84.31372549
14 XGBoost Validación Cruzada SIN SMOTE 1 91.68975069
4 CatBoost Validación Cruzada SIN SMOTE 1 84.87394958
9 CatBoost Validación Cruzada SIN SMOTE 1 83.65650970
19 XGBoost Validación Cruzada SIN SMOTE 1 92.17877095
Recall_Train AUPRC_Train Accuracy_Train F1-Score_Train Precision_Test \
18 95.89442815 98.10093684 99.97478331 92.76595745 94.28571429
8 87.09677419 89.22671407 99.94659995 84.61538462 91.66666667
15 96.47058824 98.39878875 99.97873876 93.84835479 84.61538462
6 88.52941176 89.63845446 99.95055526 85.75498575 94.28571429
10 96.76470588 98.47720302 99.97725542 93.46590909 86.48648649
5 87.94117647 89.82168472 99.94709413 84.82269504 80.48780488
16 96.17647059 98.26474046 99.97626653 93.16239316 96.96969697
0 88.52941176 90.30398291 99.94808303 85.14851485 79.48717949
17 97.05882353 98.34685672 99.97972766 94.15121255 83.78378378
7 88.52941176 89.94478833 99.95006082 85.63300142 78.04878049
12 97.64705882 98.12995576 99.97478318 92.86713287 91.42857143
13 96.47058824 98.30992168 99.97824432 93.71428571 87.09677419
2 89.11764706 90.96548310 99.94956637 85.59322034 86.48648649
3 88.52941176 90.10039711 99.94907192 85.39007092 83.33333333
11 97.05882353 98.73492230 99.98022211 94.28571429 90.62500000
1 88.52941176 90.45336479 99.95302750 86.37015782 83.33333333
14 97.35294118 98.73549965 99.98071655 94.43651926 75.60975610
4 89.11764706 90.34495990 99.95500529 86.94404591 65.95744681
9 88.56304985 91.10534195 99.95154440 86.03988604 73.17073171
19 96.77419355 98.73992764 99.98071665 94.42060086 81.08108108
Recall_Test AUPRC_Test Accuracy_Test F1-Score_Test iterations \
18 89.18918919 95.20004893 99.97329892 91.66666667 NaN
8 89.18918919 92.66666056 99.96884874 90.41095890 300
15 86.84210526 89.80222597 99.95105020 85.71428571 NaN
6 86.84210526 87.95879951 99.96885012 90.41095890 300
10 84.21052632 87.93778215 99.95105020 85.33333333 NaN
5 86.84210526 87.70971563 99.94215023 83.54430380 300
16 84.21052632 87.49442362 99.96885012 90.14084507 NaN
0 81.57894737 86.04299367 99.93325027 80.51948052 300
17 81.57894737 85.79430745 99.94215023 82.66666667 NaN
7 84.21052632 83.72146354 99.93325027 81.01265823 300
12 84.21052632 83.62324257 99.95995016 87.67123288 NaN
13 71.05263158 83.01005491 99.93325027 78.26086957 NaN
2 84.21052632 82.43458697 99.95105020 85.33333333 300
3 78.94736842 82.11332010 99.93770025 81.08108108 300
11 76.31578947 81.86427842 99.94660021 82.85714286 NaN
1 78.94736842 81.38894519 99.93770025 81.08108108 300
14 81.57894737 79.57447254 99.92435030 78.48101266 NaN
4 81.57894737 77.19532879 99.89765041 72.94117647 300
9 81.08108108 76.25344665 99.91989676 76.92307692 300
19 81.08108108 75.75449585 99.93769748 81.08108108 NaN
learning_rate depth class_weights verbose max_depth n_estimators \
18 NaN NaN NaN NaN None None
8 0.03000000 4 [1, 10] 50 None None
15 NaN NaN NaN NaN None None
6 0.03000000 4 [1, 10] 50 None None
10 NaN NaN NaN NaN None None
5 0.03000000 4 [1, 10] 50 None None
16 NaN NaN NaN NaN None None
0 0.03000000 4 [1, 10] 50 None None
17 NaN NaN NaN NaN None None
7 0.03000000 4 [1, 10] 50 None None
12 NaN NaN NaN NaN None None
13 NaN NaN NaN NaN None None
2 0.03000000 4 [1, 10] 50 None None
3 0.03000000 4 [1, 10] 50 None None
11 NaN NaN NaN NaN None None
1 0.03000000 4 [1, 10] 50 None None
14 NaN NaN NaN NaN None None
4 0.03000000 4 [1, 10] 50 None None
9 0.03000000 4 [1, 10] 50 None None
19 NaN NaN NaN NaN NaN NaN
scale_pos_weight min_child_weight gamma l2_leaf_reg subsample Fold \
18 None NaN NaN NaN None 9
8 None NaN NaN 10.00000000 None 9
15 None NaN NaN NaN None 6
6 None NaN NaN 10.00000000 None 7
10 None NaN NaN NaN None 1
5 None NaN NaN 10.00000000 None 6
16 None NaN NaN NaN None 7
0 None NaN NaN 10.00000000 None 1
17 None NaN NaN NaN None 8
7 None NaN NaN 10.00000000 None 8
12 None NaN NaN NaN None 3
13 None NaN NaN NaN None 4
2 None NaN NaN 10.00000000 None 3
3 None NaN NaN 10.00000000 None 4
11 None NaN NaN NaN None 2
1 None NaN NaN 10.00000000 None 2
14 None NaN NaN NaN None 5
4 None NaN NaN 10.00000000 None 5
9 None NaN NaN 10.00000000 None 10
19 NaN NaN NaN NaN NaN 10
model_shrink_mode per_feature_ctr eta devices \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
per_object_feature_penalties allow_const_label mvs_reg \
18 None None None
8 None None None
15 None None None
6 None None None
10 None None None
5 None None None
16 None None None
0 None None None
17 None None None
7 None None None
12 None None None
13 None None None
2 None None None
3 None None None
11 None None None
1 None None None
14 None None None
4 None None None
9 None None None
19 NaN NaN NaN
dev_score_calc_obj_block_size ctr_leaf_count_limit max_ctr_complexity \
18 None None None
8 None None None
15 None None None
6 None None None
10 None None None
5 None None None
16 None None None
0 None None None
17 None None None
7 None None None
12 None None None
13 None None None
2 None None None
3 None None None
11 None None None
1 None None None
14 None None None
4 None None None
9 None None None
19 NaN NaN NaN
target_border metric_period eval_fraction allow_writing_files \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
save_snapshot classes_count ctr_description leaf_estimation_method \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
one_hot_max_size min_data_in_leaf random_score_type colsample_bylevel \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
bootstrap_type custom_metric thread_count bagging_temperature \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
random_strength nan_mode text_features per_float_feature_quantization \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
simple_ctr output_borders use_best_model gpu_cat_features_storage \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
combinations_ctr border_count feature_border_type data_partition \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
fold_permutation_block od_pval name early_stopping_rounds tokenizers \
18 None None None None None
8 None None None None None
15 None None None None None
6 None None None None None
10 None None None None None
5 None None None None None
16 None None None None None
0 None None None None None
17 None None None None None
7 None None None None None
12 None None None None None
13 None None None None None
2 None None None None None
3 None None None None None
11 None None None None None
1 None None None None None
14 None None None None None
4 None None None None None
9 None None None None None
19 NaN NaN NaN NaN NaN
best_model_min_trees dev_efb_max_buckets feature_weights \
18 None None None
8 None None None
15 None None None
6 None None None
10 None None None
5 None None None
16 None None None
0 None None None
17 None None None
7 None None None
12 None None None
13 None None None
2 None None None
3 None None None
11 None None None
1 None None None
14 None None None
4 None None None
9 None None None
19 NaN NaN NaN
posterior_sampling metadata boosting_type diffusion_temperature \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
gpu_ram_part score_function approx_on_full_history sampling_unit task_type \
18 None None None None None
8 None None None None None
15 None None None None None
6 None None None None None
10 None None None None None
5 None None None None None
16 None None None None None
0 None None None None None
17 None None None None None
7 None None None None None
12 None None None None None
13 None None None None None
2 None None None None None
3 None None None None None
11 None None None None None
1 None None None None None
14 None None None None None
4 None None None None None
9 None None None None None
19 NaN NaN NaN NaN NaN
snapshot_interval rsm store_all_simple_ctr random_seed \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
sampling_frequency ctr_target_border_count final_ctr_computation_mode \
18 None None None
8 None None None
15 None None None
6 None None None
10 None None None
5 None None None
16 None None None
0 None None None
17 None None None
7 None None None
12 None None None
13 None None None
2 None None None
3 None None None
11 None None None
1 None None None
14 None None None
4 None None None
9 None None None
19 NaN NaN NaN
fixed_binary_splits auto_class_weights ctr_history_unit device_config \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
leaf_estimation_backtracking has_time fold_len_multiplier \
18 None None None
8 None None None
15 None None None
6 None None None
10 None None None
5 None None None
16 None None None
0 None None None
17 None None None
7 None None None
12 None None None
13 None None None
2 None None None
3 None None None
11 None None None
1 None None None
14 None None None
4 None None None
9 None None None
19 NaN NaN NaN
pinned_memory_size feature_calcers model_shrink_rate od_type \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
monotone_constraints dictionaries max_bin boost_from_average grow_policy \
18 None None None None None
8 None None None None None
15 None None None None None
6 None None None None None
10 None None None None None
5 None None None None None
16 None None None None None
0 None None None None None
17 None None None None None
7 None None None None None
12 None None None None None
13 None None None None None
2 None None None None None
3 None None None None None
11 None None None None None
1 None None None None None
14 None None None None None
4 None None None None None
9 None None None None None
19 NaN NaN NaN NaN NaN
embedding_features langevin callback cat_features train_dir \
18 None None None None None
8 None None None None None
15 None None None None None
6 None None None None None
10 None None None None None
5 None None None None None
16 None None None None None
0 None None None None None
17 None None None None None
7 None None None None None
12 None None None None None
13 None None None None None
2 None None None None None
3 None None None None None
11 None None None None None
1 None None None None None
14 None None None None None
4 None None None None None
9 None None None None None
19 NaN NaN NaN NaN NaN
sparse_features_conflict_fraction ignored_features num_trees \
18 None None None
8 None None None
15 None None None
6 None None None
10 None None None
5 None None None
16 None None None
0 None None None
17 None None None
7 None None None
12 None None None
13 None None None
2 None None None
3 None None None
11 None None None
1 None None None
14 None None None
4 None None None
9 None None None
19 NaN NaN NaN
penalties_coefficient objective used_ram_limit text_processing reg_lambda \
18 None None None None None
8 None None None None None
15 None None None None None
6 None None None None None
10 None None None None None
5 None None None None None
16 None None None None None
0 None None None None None
17 None None None None None
7 None None None None None
12 None None None None None
13 None None None None None
2 None None None None None
3 None None None None None
11 None None None None None
1 None None None None None
14 None None None None None
4 None None None None None
9 None None None None None
19 NaN None NaN NaN NaN
snapshot_file random_state custom_loss loss_function \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
leaf_estimation_iterations silent max_leaves input_borders \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
counter_calc_method num_boost_round model_size_reg eval_metric num_leaves \
18 None None None None None
8 None None None None None
15 None None None None None
6 None None None None None
10 None None None None None
5 None None None None None
16 None None None None None
0 None None None None None
17 None None None None None
7 None None None None None
12 None None None None None
13 None None None None None
2 None None None None None
3 None None None None None
11 None None None None None
1 None None None None None
14 None None None None None
4 None None None None None
9 None None None None None
19 NaN NaN NaN NaN NaN
min_child_samples class_names logging_level first_feature_use_penalties \
18 None None None None
8 None None None None
15 None None None None
6 None None None None
10 None None None None
5 None None None None
16 None None None None
0 None None None None
17 None None None None
7 None None None None
12 None None None None
13 None None None None
2 None None None None
3 None None None None
11 None None None None
1 None None None None
14 None None None None
4 None None None None
9 None None None None
19 NaN NaN NaN NaN
od_wait kwargs
18 None NaN
8 None NaN
15 None NaN
6 None NaN
10 None NaN
5 None NaN
16 None NaN
0 None NaN
17 None NaN
7 None NaN
12 None NaN
13 None NaN
2 None NaN
3 None NaN
11 None NaN
1 None NaN
14 None NaN
4 None NaN
9 None NaN
19 NaN None
✅ Resultados guardados en 'resultados_validacion_cruzada_sin_smote.csv'
CPU times: user 7min 56s, sys: 8.84 s, total: 8min 5s
Wall time: 5min 5s
4. Optimización de Hiperparámetros con GridSearchCV (con SMOTE)¶
GridSearchCV permite probar una combinación exhaustiva de hiperparámetros para encontrar la mejor configuración.
In [ ]:
# ==========================================================
# GridSearchCV con SMOTE en Pipeline
# ==========================================================
%%time
from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline as ImbPipeline
from xgboost import XGBClassifier
from catboost import CatBoostClassifier
import pandas as pd
# ================================================
# 1. División del Dataset
# ================================================
print("\n🔄 Dividiendo el conjunto de datos en entrenamiento y prueba...")
X_train_full, X_test, y_train_full, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
print(f"✅ Tamaño de entrenamiento: {len(X_train_full)} | Tamaño de prueba: {len(X_test)}")
# ================================================
# 2. Configuración de Parámetros
# ================================================
param_grid_xgb = {
'classifier__learning_rate': [0.01, 0.05],
'classifier__max_depth': [3, 4],
'classifier__n_estimators': [100, 200],
'classifier__scale_pos_weight': [5, 10]
}
param_grid_catboost = {
'classifier__iterations': [200, 300],
'classifier__learning_rate': [0.01, 0.05],
'classifier__depth': [4, 6],
'classifier__class_weights': [[1, 50]]
}
# ================================================
# 3. Inicialización del DataFrame de Resultados
# ================================================
columnas_resultados = [
'Modelo', 'Tecnica', 'Sobreajuste',
'Precision_Train', 'Recall_Train', 'AUPRC_Train', 'Accuracy_Train', 'F1-Score_Train',
'Precision_Test', 'Recall_Test', 'AUPRC_Test', 'Accuracy_Test', 'F1-Score_Test',
'learning_rate', 'max_depth', 'n_estimators', 'scale_pos_weight',
'iterations', 'depth', 'class_weights', 'Fold'
]
resultados_gridsearch_con_smote = pd.DataFrame(columns=columnas_resultados)
# ================================================
# 4. Función GridSearchCV con SMOTE en Pipeline
# ================================================
def gridsearch_cv_con_smote_pipeline(modelo, param_grid, nombre_modelo, resultados_df):
print(f"\n🚀 Iniciando GridSearchCV con SMOTE para {nombre_modelo}...")
# Configuración del pipeline con SMOTE
pipeline = ImbPipeline([
('smote', SMOTE(random_state=42)),
('classifier', modelo())
])
# Configuración de GridSearchCV con validación cruzada
grid_search = GridSearchCV(
estimator=pipeline,
param_grid=param_grid,
scoring='average_precision',
cv=StratifiedKFold(n_splits=3, shuffle=True, random_state=42),
verbose=2,
n_jobs=-1
)
# Ejecución del GridSearchCV
grid_search.fit(X_train_full, y_train_full)
# Obtener los mejores parámetros encontrados
mejores_parametros = grid_search.best_params_
print(f"\n✅ Mejores parámetros para {nombre_modelo}: {mejores_parametros}")
# Evaluación del modelo final con los mejores parámetros
print("\n📈 Evaluando el modelo final con los mejores hiperparámetros...")
mejores_parametros = {k.split("__")[-1]: v for k, v in mejores_parametros.items()} # Limpiar nombres de parámetros
resultados_df = entrenar_y_evaluar(
modelo=modelo,
nombre_modelo=nombre_modelo,
parametros=mejores_parametros,
X_train=X_train_full,
y_train=y_train_full,
X_test=X_test,
y_test=y_test,
tecnica="GridSearchCV con SMOTE",
resultados_df=resultados_df
)
return resultados_df
# ================================================
# 5. Optimización y Evaluación
# ================================================
# Ejecutar GridSearchCV para XGBoost
print("\n🚀 GridSearchCV para XGBoost...")
resultados_gridsearch_con_smote = gridsearch_cv_con_smote_pipeline(
XGBClassifier, param_grid_xgb, "XGBoost", resultados_gridsearch_con_smote
)
# Ejecutar GridSearchCV para CatBoost
print("\n🚀 GridSearchCV para CatBoost...")
resultados_gridsearch_con_smote = gridsearch_cv_con_smote_pipeline(
CatBoostClassifier, param_grid_catboost, "CatBoost", resultados_gridsearch_con_smote
)
# ================================================
# 6. Consolidar y Guardar Resultados
# ================================================
print("\n📊 Consolidando y ordenando resultados...")
resultados_ordenados = resultados_gridsearch_con_smote.sort_values(
by=['AUPRC_Test', 'Recall_Test', 'Precision_Test', 'F1-Score_Test'],
ascending=[False, False, False, False]
)
print("\n🏆 Resultados Finales Ordenados:")
print(resultados_ordenados)
# Guardar resultados en CSV
output_file = "resultados_gridsearch_con_smote.csv"
resultados_ordenados.to_csv(output_file, index=False)
print(f"\n✅ Resultados guardados en '{output_file}'")
🔄 Dividiendo el conjunto de datos en entrenamiento y prueba...
✅ Tamaño de entrenamiento: 179774 | Tamaño de prueba: 44944
🚀 GridSearchCV para XGBoost...
🚀 Iniciando GridSearchCV con SMOTE para XGBoost...
Fitting 3 folds for each of 16 candidates, totalling 48 fits
✅ Mejores parámetros para XGBoost: {'classifier__learning_rate': 0.01, 'classifier__max_depth': 3, 'classifier__n_estimators': 100, 'classifier__scale_pos_weight': 5}
📈 Evaluando el modelo final con los mejores hiperparámetros...
🚀 Entrenando XGBoost (GridSearchCV con SMOTE)...
✅ Resultados para XGBoost (GridSearchCV con SMOTE):
- Modelo: XGBoost
- Tecnica: GridSearchCV con SMOTE
- Sobreajuste: 0
- Precision_Train: 85.71
- Recall_Train: 83.44
- AUPRC_Train: 82.03
- Accuracy_Train: 99.95
- F1-Score_Train: 84.56
- Precision_Test: 86.15
- Recall_Test: 73.68
- AUPRC_Test: 76.72
- Accuracy_Test: 99.94
- F1-Score_Test: 79.43
- objective: None
- kwargs: None
✅ Tamaño del DataFrame actualizado: (1, 23)
🚀 GridSearchCV para CatBoost...
🚀 Iniciando GridSearchCV con SMOTE para CatBoost...
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✅ Mejores parámetros para CatBoost: {'classifier__class_weights': [1, 50], 'classifier__depth': 4, 'classifier__iterations': 200, 'classifier__learning_rate': 0.05}
📈 Evaluando el modelo final con los mejores hiperparámetros...
🚀 Entrenando CatBoost (GridSearchCV con SMOTE)...
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✅ Resultados para CatBoost (GridSearchCV con SMOTE):
- Modelo: CatBoost
- Tecnica: GridSearchCV con SMOTE
- Sobreajuste: 1
- Precision_Train: 72.07
- Recall_Train: 95.70
- AUPRC_Train: 89.09
- Accuracy_Train: 99.93
- F1-Score_Train: 82.22
- Precision_Test: 68.13
- Recall_Test: 81.58
- AUPRC_Test: 75.57
- Accuracy_Test: 99.90
- F1-Score_Test: 74.25
- model_shrink_mode: None
- per_feature_ctr: None
- eta: None
- devices: None
- verbose: None
- per_object_feature_penalties: None
- allow_const_label: None
- mvs_reg: None
- dev_score_calc_obj_block_size: None
- ctr_leaf_count_limit: None
- max_ctr_complexity: None
- target_border: None
- metric_period: None
- depth: 4
- eval_fraction: None
- allow_writing_files: None
- save_snapshot: None
- classes_count: None
- ctr_description: None
- leaf_estimation_method: None
- one_hot_max_size: None
- min_data_in_leaf: None
- iterations: 200
- max_depth: None
- random_score_type: None
- colsample_bylevel: None
- bootstrap_type: None
- n_estimators: None
- custom_metric: None
- thread_count: None
- bagging_temperature: None
- random_strength: None
- nan_mode: None
- text_features: None
- per_float_feature_quantization: None
- simple_ctr: None
- output_borders: None
- use_best_model: None
- gpu_cat_features_storage: None
- combinations_ctr: None
- border_count: None
- feature_border_type: None
- data_partition: None
- fold_permutation_block: None
- od_pval: None
- name: None
- early_stopping_rounds: None
- tokenizers: None
- best_model_min_trees: None
- dev_efb_max_buckets: None
- feature_weights: None
- posterior_sampling: None
- metadata: None
- boosting_type: None
- diffusion_temperature: None
- gpu_ram_part: None
- score_function: None
- approx_on_full_history: None
- sampling_unit: None
- learning_rate: 0.05
- task_type: None
- snapshot_interval: None
- rsm: None
- store_all_simple_ctr: None
- random_seed: None
- sampling_frequency: None
- ctr_target_border_count: None
- final_ctr_computation_mode: None
- fixed_binary_splits: None
- subsample: None
- auto_class_weights: None
- ctr_history_unit: None
- device_config: None
- leaf_estimation_backtracking: None
- l2_leaf_reg: None
- has_time: None
- fold_len_multiplier: None
- pinned_memory_size: None
- feature_calcers: None
- model_shrink_rate: None
- od_type: None
- monotone_constraints: None
- dictionaries: None
- class_weights: [1, 50]
- max_bin: None
- boost_from_average: None
- grow_policy: None
- embedding_features: None
- langevin: None
- callback: None
- cat_features: None
- train_dir: None
- sparse_features_conflict_fraction: None
- ignored_features: None
- num_trees: None
- penalties_coefficient: None
- objective: None
- used_ram_limit: None
- text_processing: None
- reg_lambda: None
- snapshot_file: None
- random_state: None
- custom_loss: None
- loss_function: None
- leaf_estimation_iterations: None
- silent: None
- max_leaves: None
- input_borders: None
- counter_calc_method: None
- num_boost_round: None
- model_size_reg: None
- eval_metric: None
- num_leaves: None
- min_child_samples: None
- class_names: None
- scale_pos_weight: None
- logging_level: None
- first_feature_use_penalties: None
- od_wait: None
✅ Tamaño del DataFrame actualizado: (2, 134)
📊 Consolidando y ordenando resultados...
🏆 Resultados Finales Ordenados:
Modelo Tecnica Sobreajuste Precision_Train \
0 XGBoost GridSearchCV con SMOTE 0 85.71428571
1 CatBoost GridSearchCV con SMOTE 1 72.06982544
Recall_Train AUPRC_Train Accuracy_Train F1-Score_Train Precision_Test \
0 83.44370861 82.02573556 99.94882464 84.56375839 86.15384615
1 95.69536424 89.08783454 99.93046825 82.21906117 68.13186813
Recall_Test AUPRC_Test Accuracy_Test F1-Score_Test learning_rate \
0 73.68421053 76.72330556 99.93547526 79.43262411 NaN
1 81.57894737 75.56758375 99.90432538 74.25149701 0.05000000
max_depth n_estimators scale_pos_weight iterations depth class_weights Fold \
0 NaN NaN NaN NaN NaN NaN NaN
1 None None None 200 4 [1, 50] NaN
objective kwargs model_shrink_mode per_feature_ctr eta devices verbose \
0 None None NaN NaN NaN NaN NaN
1 None NaN None None None None None
per_object_feature_penalties allow_const_label mvs_reg \
0 NaN NaN NaN
1 None None None
dev_score_calc_obj_block_size ctr_leaf_count_limit max_ctr_complexity \
0 NaN NaN NaN
1 None None None
target_border metric_period eval_fraction allow_writing_files save_snapshot \
0 NaN NaN NaN NaN NaN
1 None None None None None
classes_count ctr_description leaf_estimation_method one_hot_max_size \
0 NaN NaN NaN NaN
1 None None None None
min_data_in_leaf random_score_type colsample_bylevel bootstrap_type \
0 NaN NaN NaN NaN
1 None None None None
custom_metric thread_count bagging_temperature random_strength nan_mode \
0 NaN NaN NaN NaN NaN
1 None None None None None
text_features per_float_feature_quantization simple_ctr output_borders \
0 NaN NaN NaN NaN
1 None None None None
use_best_model gpu_cat_features_storage combinations_ctr border_count \
0 NaN NaN NaN NaN
1 None None None None
feature_border_type data_partition fold_permutation_block od_pval name \
0 NaN NaN NaN NaN NaN
1 None None None None None
early_stopping_rounds tokenizers best_model_min_trees dev_efb_max_buckets \
0 NaN NaN NaN NaN
1 None None None None
feature_weights posterior_sampling metadata boosting_type \
0 NaN NaN NaN NaN
1 None None None None
diffusion_temperature gpu_ram_part score_function approx_on_full_history \
0 NaN NaN NaN NaN
1 None None None None
sampling_unit task_type snapshot_interval rsm store_all_simple_ctr \
0 NaN NaN NaN NaN NaN
1 None None None None None
random_seed sampling_frequency ctr_target_border_count \
0 NaN NaN NaN
1 None None None
final_ctr_computation_mode fixed_binary_splits subsample auto_class_weights \
0 NaN NaN NaN NaN
1 None None None None
ctr_history_unit device_config leaf_estimation_backtracking l2_leaf_reg \
0 NaN NaN NaN NaN
1 None None None None
has_time fold_len_multiplier pinned_memory_size feature_calcers \
0 NaN NaN NaN NaN
1 None None None None
model_shrink_rate od_type monotone_constraints dictionaries max_bin \
0 NaN NaN NaN NaN NaN
1 None None None None None
boost_from_average grow_policy embedding_features langevin callback \
0 NaN NaN NaN NaN NaN
1 None None None None None
cat_features train_dir sparse_features_conflict_fraction ignored_features \
0 NaN NaN NaN NaN
1 None None None None
num_trees penalties_coefficient used_ram_limit text_processing reg_lambda \
0 NaN NaN NaN NaN NaN
1 None None None None None
snapshot_file random_state custom_loss loss_function \
0 NaN NaN NaN NaN
1 None None None None
leaf_estimation_iterations silent max_leaves input_borders \
0 NaN NaN NaN NaN
1 None None None None
counter_calc_method num_boost_round model_size_reg eval_metric num_leaves \
0 NaN NaN NaN NaN NaN
1 None None None None None
min_child_samples class_names logging_level first_feature_use_penalties \
0 NaN NaN NaN NaN
1 None None None None
od_wait
0 NaN
1 None
✅ Resultados guardados en 'resultados_gridsearch_con_smote.csv'
CPU times: user 1min 16s, sys: 3.65 s, total: 1min 19s
Wall time: 13min 57s
5. Optimización de Hiperparámetros con Optuna (con SMOTE)¶
Optimización de Hiperparámetros con Optuna:
- Se aplica SMOTE en cada iteración de validación cruzada para equilibrar los datos.
- La función optimizar_optuna ajusta los hiperparámetros de los modelos buscando maximizar la métrica AUPRC.
- Se registran las métricas clave y los mejores parámetros para cada modelo.
Los resultados finales se ordenan, filtran y se guardan en un archivo CSV.
Optuna es más eficiente que GridSearchCV porque utiliza búsqueda bayesiana para explorar el espacio de hiperparámetros de manera inteligente.
In [ ]:
# ==========================================================
# Optimización de Hiperparámetros con Optuna y SMOTE
# ==========================================================
import optuna
import numpy as np
from sklearn.model_selection import StratifiedKFold
from imblearn.over_sampling import SMOTE
from xgboost import XGBClassifier
from catboost import CatBoostClassifier
import pandas as pd
# ==============================
# 1. Inicialización del DataFrame de Resultados
# ==============================
columnas_resultados_optuna = [
'Modelo', 'Tecnica', 'Fold', 'Sobreajuste',
'Precision_Train', 'Recall_Train', 'AUPRC_Train', 'Accuracy_Train', 'F1-Score_Train',
'Precision_Test', 'Recall_Test', 'AUPRC_Test', 'Accuracy_Test', 'F1-Score_Test',
'learning_rate', 'max_depth', 'n_estimators', 'scale_pos_weight', 'iterations', 'class_weights'
]
resultados_optuna = pd.DataFrame(columns=columnas_resultados_optuna)
# ==============================
# 2. Función optimizar_optuna adaptada
# ==============================
def optimizar_optuna(trial, modelo, nombre_modelo, X_train, y_train, resultados_df):
"""
Optimiza hiperparámetros usando Optuna y valida con SMOTE y StratifiedKFold.
Guarda todas las métricas y parámetros en un DataFrame.
"""
print(f"\n🔍 Optimizando hiperparámetros para {nombre_modelo} con Optuna...")
# Hiperparámetros sugeridos por Optuna
parametros = {
'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 0.1),
'max_depth': trial.suggest_int('max_depth', 3, 6),
'n_estimators': trial.suggest_int('n_estimators', 100, 300),
'scale_pos_weight': trial.suggest_float('scale_pos_weight', 5, 15),
}
kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)
scores = []
# Evaluación en cada fold
for fold, (train_idx, test_idx) in enumerate(kfold.split(X_train, y_train)):
print(f"\n🔄 Fold {fold + 1}: Optimización en progreso...")
# Dividir datos
X_train_fold, X_test_fold = X_train.iloc[train_idx], X_train.iloc[test_idx]
y_train_fold, y_test_fold = y_train.iloc[train_idx], y_train.iloc[test_idx]
# Aplicar SMOTE
print(f"📊 Antes de SMOTE: {y_train_fold.value_counts(normalize=True)}")
smote = SMOTE(random_state=42)
X_train_res, y_train_res = smote.fit_resample(X_train_fold, y_train_fold)
print(f"📈 Después de SMOTE: {y_train_res.value_counts(normalize=True)}")
# Entrenar y evaluar el modelo
resultados_df = entrenar_y_evaluar(
modelo=modelo,
nombre_modelo=nombre_modelo,
parametros=parametros,
X_train=X_train_res,
y_train=y_train_res,
X_test=X_test_fold,
y_test=y_test_fold,
tecnica="Optuna con SMOTE",
resultados_df=resultados_df
)
# Guardar la métrica AUPRC
score = resultados_df['AUPRC_Test'].iloc[-1]
scores.append(score)
# Promedio de AUPRC en los folds
mean_score = np.mean(scores)
print(f"\n🏆 Promedio de AUPRC en validación cruzada: {mean_score:.4f}")
return mean_score
# ==============================
# 3. Optimización con Optuna
# ==============================
# ==============================
# Optuna para XGBoost
# ==============================
print("\n🚀 Optimización de Hiperparámetros con Optuna para XGBoost...")
study_xgb = optuna.create_study(direction='maximize')
study_xgb.optimize(
lambda trial: optimizar_optuna(trial, XGBClassifier, "XGBoost", X_train, y_train, resultados_optuna),
n_trials=50
)
# ==============================
# Optuna para CatBoost
# ==============================
print("\n🚀 Optimización de Hiperparámetros con Optuna para CatBoost...")
study_catboost = optuna.create_study(direction='maximize')
study_catboost.optimize(
lambda trial: optimizar_optuna(trial, CatBoostClassifier, "CatBoost", X_train, y_train, resultados_optuna),
n_trials=50
)
# ==============================
# 4. Consolidar y Guardar Resultados
# ==============================
print("\n🔍 Consolidando y ordenando resultados...")
resultados_ordenados = resultados_optuna.sort_values(
by=['AUPRC_Test', 'Recall_Test', 'Precision_Test', 'F1-Score_Test'],
ascending=[False, False, False, False]
)
print("\n🏆 Resultados Finales Ordenados:")
print(resultados_ordenados)
# Guardar resultados
output_file = "resultados_optuna_con_smote.csv"
resultados_ordenados.to_csv(output_file, index=False)
print(f"\n✅ Resultados guardados en '{output_file}'")
[I 2024-12-19 13:22:34,041] A new study created in memory with name: no-name-8e0e9dbf-6367-41a2-aecd-248cd5ebcaaf
🚀 Optimización de Hiperparámetros con Optuna para XGBoost... 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.65 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 98.79 - F1-Score_Train: 98.81 - Precision_Test: 5.66 - Recall_Test: 89.68 - AUPRC_Test: 76.96 - Accuracy_Test: 97.47 - F1-Score_Test: 10.65 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 96.64 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 98.26 - F1-Score_Train: 98.29 - Precision_Test: 4.38 - Recall_Test: 94.44 - AUPRC_Test: 76.63 - Accuracy_Test: 96.52 - F1-Score_Test: 8.38 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:23:02,583] Trial 0 finished with value: 76.27242310152417 and parameters: {'learning_rate': 0.06946814045829992, 'max_depth': 4, 'n_estimators': 111, 'scale_pos_weight': 10.849281219534213}. Best is trial 0 with value: 76.27242310152417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.32 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 98.62 - F1-Score_Train: 98.64 - Precision_Test: 5.13 - Recall_Test: 88.89 - AUPRC_Test: 75.22 - Accuracy_Test: 97.22 - F1-Score_Test: 9.70 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 76.2724 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 94.58 - Recall_Train: 99.71 - AUPRC_Train: 99.81 - Accuracy_Train: 97.00 - F1-Score_Train: 97.08 - Precision_Test: 2.55 - Recall_Test: 90.48 - AUPRC_Test: 75.17 - Accuracy_Test: 94.17 - F1-Score_Test: 4.96 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 93.55 - Recall_Train: 98.85 - AUPRC_Train: 99.68 - Accuracy_Train: 96.02 - F1-Score_Train: 96.13 - Precision_Test: 2.31 - Recall_Test: 95.24 - AUPRC_Test: 68.69 - Accuracy_Test: 93.22 - F1-Score_Test: 4.51 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:23:39,083] Trial 1 finished with value: 73.28403168120853 and parameters: {'learning_rate': 0.01877718389352851, 'max_depth': 3, 'n_estimators': 215, 'scale_pos_weight': 5.1617134561936755}. Best is trial 0 with value: 76.27242310152417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 94.55 - Recall_Train: 99.07 - AUPRC_Train: 99.73 - Accuracy_Train: 96.68 - F1-Score_Train: 96.76 - Precision_Test: 2.61 - Recall_Test: 91.27 - AUPRC_Test: 75.99 - Accuracy_Test: 94.25 - F1-Score_Test: 5.07 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 73.2840 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.44 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.21 - F1-Score_Train: 99.21 - Precision_Test: 8.47 - Recall_Test: 88.89 - AUPRC_Test: 79.00 - Accuracy_Test: 98.36 - F1-Score_Test: 15.46 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.60 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 98.77 - F1-Score_Train: 98.79 - Precision_Test: 6.04 - Recall_Test: 93.65 - AUPRC_Test: 77.51 - Accuracy_Test: 97.54 - F1-Score_Test: 11.35 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:24:13,357] Trial 2 finished with value: 79.11833773313246 and parameters: {'learning_rate': 0.09268097642588863, 'max_depth': 3, 'n_estimators': 181, 'scale_pos_weight': 12.430971158144311}. Best is trial 2 with value: 79.11833773313246.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.15 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.06 - F1-Score_Train: 99.07 - Precision_Test: 7.36 - Recall_Test: 88.89 - AUPRC_Test: 80.85 - Accuracy_Test: 98.10 - F1-Score_Test: 13.59 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 79.1183 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.50 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.75 - F1-Score_Train: 99.75 - Precision_Test: 19.12 - Recall_Test: 86.51 - AUPRC_Test: 81.69 - Accuracy_Test: 99.36 - F1-Score_Test: 31.32 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.01 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.50 - F1-Score_Train: 99.50 - Precision_Test: 12.39 - Recall_Test: 89.68 - AUPRC_Test: 79.80 - Accuracy_Test: 98.92 - F1-Score_Test: 21.77 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:25:14,604] Trial 3 finished with value: 81.49583968883515 and parameters: {'learning_rate': 0.05439196518814538, 'max_depth': 4, 'n_estimators': 279, 'scale_pos_weight': 10.400533011658881}. Best is trial 3 with value: 81.49583968883515.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.31 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.65 - F1-Score_Train: 99.65 - Precision_Test: 16.52 - Recall_Test: 87.30 - AUPRC_Test: 82.99 - Accuracy_Test: 99.24 - F1-Score_Test: 27.78 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 81.4958 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.65 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.82 - F1-Score_Train: 99.83 - Precision_Test: 22.55 - Recall_Test: 85.71 - AUPRC_Test: 82.84 - Accuracy_Test: 99.48 - F1-Score_Test: 35.70 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.51 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.75 - F1-Score_Train: 99.75 - Precision_Test: 20.64 - Recall_Test: 86.51 - AUPRC_Test: 79.96 - Accuracy_Test: 99.42 - F1-Score_Test: 33.33 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:26:06,517] Trial 4 finished with value: 82.01437290523499 and parameters: {'learning_rate': 0.04601648510038596, 'max_depth': 6, 'n_estimators': 185, 'scale_pos_weight': 7.5841982122007}. Best is trial 4 with value: 82.01437290523499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.67 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.83 - F1-Score_Train: 99.83 - Precision_Test: 26.33 - Recall_Test: 86.51 - AUPRC_Test: 83.24 - Accuracy_Test: 99.57 - F1-Score_Test: 40.37 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 82.0144 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.64 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.82 - F1-Score_Train: 99.82 - Precision_Test: 23.34 - Recall_Test: 86.51 - AUPRC_Test: 81.07 - Accuracy_Test: 99.50 - F1-Score_Test: 36.76 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.38 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.69 - F1-Score_Train: 99.69 - Precision_Test: 17.32 - Recall_Test: 88.10 - AUPRC_Test: 79.45 - Accuracy_Test: 99.27 - F1-Score_Test: 28.94 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:26:54,135] Trial 5 finished with value: 81.5907783938854 and parameters: {'learning_rate': 0.0667410689205582, 'max_depth': 4, 'n_estimators': 248, 'scale_pos_weight': 5.874528406175232}. Best is trial 4 with value: 82.01437290523499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.56 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.78 - F1-Score_Train: 99.78 - Precision_Test: 22.63 - Recall_Test: 87.30 - AUPRC_Test: 84.26 - Accuracy_Test: 99.48 - F1-Score_Test: 35.95 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 81.5908 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 93.87 - Recall_Train: 99.98 - AUPRC_Train: 99.89 - Accuracy_Train: 96.72 - F1-Score_Train: 96.83 - Precision_Test: 2.32 - Recall_Test: 92.06 - AUPRC_Test: 75.75 - Accuracy_Test: 93.48 - F1-Score_Test: 4.53 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 90.89 - Recall_Train: 99.92 - AUPRC_Train: 99.85 - Accuracy_Train: 94.95 - F1-Score_Train: 95.19 - Precision_Test: 1.60 - Recall_Test: 96.03 - AUPRC_Test: 71.41 - Accuracy_Test: 90.04 - F1-Score_Test: 3.14 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:27:42,546] Trial 6 finished with value: 73.8456849423154 and parameters: {'learning_rate': 0.01294761187895525, 'max_depth': 4, 'n_estimators': 263, 'scale_pos_weight': 10.762658924116758}. Best is trial 4 with value: 82.01437290523499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 94.04 - Recall_Train: 99.91 - AUPRC_Train: 99.85 - Accuracy_Train: 96.79 - F1-Score_Train: 96.89 - Precision_Test: 2.38 - Recall_Test: 91.27 - AUPRC_Test: 74.38 - Accuracy_Test: 93.69 - F1-Score_Test: 4.64 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 73.8457 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.57 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.78 - F1-Score_Train: 99.78 - Precision_Test: 19.71 - Recall_Test: 85.71 - AUPRC_Test: 83.03 - Accuracy_Test: 99.39 - F1-Score_Test: 32.05 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.36 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.68 - F1-Score_Train: 99.68 - Precision_Test: 16.62 - Recall_Test: 89.68 - AUPRC_Test: 75.79 - Accuracy_Test: 99.23 - F1-Score_Test: 28.04 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:28:22,964] Trial 7 finished with value: 80.86364697835926 and parameters: {'learning_rate': 0.058899489581781504, 'max_depth': 6, 'n_estimators': 145, 'scale_pos_weight': 12.180673714669759}. Best is trial 4 with value: 82.01437290523499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.62 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.81 - F1-Score_Train: 99.81 - Precision_Test: 23.01 - Recall_Test: 87.30 - AUPRC_Test: 83.77 - Accuracy_Test: 99.49 - F1-Score_Test: 36.42 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 80.8636 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 93.58 - Recall_Train: 99.97 - AUPRC_Train: 99.87 - Accuracy_Train: 96.55 - F1-Score_Train: 96.67 - Precision_Test: 2.24 - Recall_Test: 92.06 - AUPRC_Test: 75.03 - Accuracy_Test: 93.23 - F1-Score_Test: 4.38 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 90.60 - Recall_Train: 99.80 - AUPRC_Train: 99.80 - Accuracy_Train: 94.72 - F1-Score_Train: 94.98 - Precision_Test: 1.54 - Recall_Test: 96.03 - AUPRC_Test: 71.26 - Accuracy_Test: 89.67 - F1-Score_Test: 3.03 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:29:07,126] Trial 8 finished with value: 73.76349796255441 and parameters: {'learning_rate': 0.014611808114790776, 'max_depth': 4, 'n_estimators': 188, 'scale_pos_weight': 8.772981912569543}. Best is trial 4 with value: 82.01437290523499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 93.67 - Recall_Train: 99.73 - AUPRC_Train: 99.81 - Accuracy_Train: 96.50 - F1-Score_Train: 96.61 - Precision_Test: 2.25 - Recall_Test: 92.06 - AUPRC_Test: 75.01 - Accuracy_Test: 93.25 - F1-Score_Test: 4.39 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 73.7635 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 92.64 - Recall_Train: 99.34 - AUPRC_Train: 99.69 - Accuracy_Train: 95.72 - F1-Score_Train: 95.87 - Precision_Test: 1.92 - Recall_Test: 92.06 - AUPRC_Test: 74.74 - Accuracy_Test: 92.07 - F1-Score_Test: 3.76 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 90.41 - Recall_Train: 98.39 - AUPRC_Train: 99.42 - Accuracy_Train: 93.98 - F1-Score_Train: 94.23 - Precision_Test: 1.52 - Recall_Test: 95.24 - AUPRC_Test: 68.79 - Accuracy_Test: 89.62 - F1-Score_Test: 3.00 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:29:45,569] Trial 9 finished with value: 72.95595731430997 and parameters: {'learning_rate': 0.010533653572554823, 'max_depth': 3, 'n_estimators': 213, 'scale_pos_weight': 5.0835849540438085}. Best is trial 4 with value: 82.01437290523499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 92.45 - Recall_Train: 98.63 - AUPRC_Train: 99.57 - Accuracy_Train: 95.29 - F1-Score_Train: 95.44 - Precision_Test: 1.88 - Recall_Test: 91.27 - AUPRC_Test: 75.33 - Accuracy_Test: 91.95 - F1-Score_Test: 3.68 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 72.9560 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.86 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.42 - F1-Score_Train: 99.43 - Precision_Test: 10.43 - Recall_Test: 86.51 - AUPRC_Test: 80.33 - Accuracy_Test: 98.73 - F1-Score_Test: 18.62 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.49 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.23 - F1-Score_Train: 99.24 - Precision_Test: 8.75 - Recall_Test: 92.06 - AUPRC_Test: 73.13 - Accuracy_Test: 98.37 - F1-Score_Test: 15.98 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:30:23,705] Trial 10 finished with value: 78.40481136872704 and parameters: {'learning_rate': 0.036192010681496446, 'max_depth': 6, 'n_estimators': 148, 'scale_pos_weight': 7.939258756385188}. Best is trial 4 with value: 82.01437290523499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.45 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.21 - F1-Score_Train: 99.22 - Precision_Test: 8.19 - Recall_Test: 88.89 - AUPRC_Test: 81.76 - Accuracy_Test: 98.30 - F1-Score_Test: 14.99 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 78.4048 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.29 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.64 - F1-Score_Train: 99.64 - Precision_Test: 15.10 - Recall_Test: 86.51 - AUPRC_Test: 82.92 - Accuracy_Test: 99.16 - F1-Score_Test: 25.71 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.91 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.45 - F1-Score_Train: 99.45 - Precision_Test: 11.40 - Recall_Test: 91.27 - AUPRC_Test: 81.19 - Accuracy_Test: 98.79 - F1-Score_Test: 20.26 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:31:16,030] Trial 11 finished with value: 82.58523136831417 and parameters: {'learning_rate': 0.03586062571213978, 'max_depth': 5, 'n_estimators': 245, 'scale_pos_weight': 7.084249372695452}. Best is trial 11 with value: 82.58523136831417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.15 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.57 - F1-Score_Train: 99.57 - Precision_Test: 14.23 - Recall_Test: 88.10 - AUPRC_Test: 83.65 - Accuracy_Test: 99.09 - F1-Score_Test: 24.50 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 82.5852 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.49 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.74 - F1-Score_Train: 99.74 - Precision_Test: 18.51 - Recall_Test: 86.51 - AUPRC_Test: 82.42 - Accuracy_Test: 99.34 - F1-Score_Test: 30.49 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.21 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.60 - F1-Score_Train: 99.60 - Precision_Test: 14.85 - Recall_Test: 89.68 - AUPRC_Test: 80.11 - Accuracy_Test: 99.12 - F1-Score_Test: 25.48 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:32:19,376] Trial 12 finished with value: 82.12714710267731 and parameters: {'learning_rate': 0.03370930150726187, 'max_depth': 5, 'n_estimators': 299, 'scale_pos_weight': 7.479130572725812}. Best is trial 11 with value: 82.58523136831417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.39 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.70 - F1-Score_Train: 99.70 - Precision_Test: 17.92 - Recall_Test: 87.30 - AUPRC_Test: 83.85 - Accuracy_Test: 99.31 - F1-Score_Test: 29.73 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 82.1271 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.48 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.23 - F1-Score_Train: 99.23 - Precision_Test: 8.42 - Recall_Test: 88.10 - AUPRC_Test: 82.18 - Accuracy_Test: 98.37 - F1-Score_Test: 15.36 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.98 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 98.97 - F1-Score_Train: 98.98 - Precision_Test: 6.80 - Recall_Test: 92.06 - AUPRC_Test: 75.95 - Accuracy_Test: 97.86 - F1-Score_Test: 12.66 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:33:23,926] Trial 13 finished with value: 80.18052914898705 and parameters: {'learning_rate': 0.025441231770727296, 'max_depth': 5, 'n_estimators': 297, 'scale_pos_weight': 14.447566507188089}. Best is trial 11 with value: 82.58523136831417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.47 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.22 - F1-Score_Train: 99.23 - Precision_Test: 8.56 - Recall_Test: 89.68 - AUPRC_Test: 82.42 - Accuracy_Test: 98.37 - F1-Score_Test: 15.63 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 80.1805 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.57 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.27 - F1-Score_Train: 99.28 - Precision_Test: 8.55 - Recall_Test: 87.30 - AUPRC_Test: 82.35 - Accuracy_Test: 98.41 - F1-Score_Test: 15.57 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.23 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.10 - F1-Score_Train: 99.11 - Precision_Test: 7.56 - Recall_Test: 91.27 - AUPRC_Test: 76.90 - Accuracy_Test: 98.11 - F1-Score_Test: 13.96 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:34:15,627] Trial 14 finished with value: 80.87054555631939 and parameters: {'learning_rate': 0.02768805545279407, 'max_depth': 5, 'n_estimators': 240, 'scale_pos_weight': 7.082951766515296}. Best is trial 11 with value: 82.58523136831417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.39 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.18 - F1-Score_Train: 99.19 - Precision_Test: 8.30 - Recall_Test: 88.89 - AUPRC_Test: 83.36 - Accuracy_Test: 98.33 - F1-Score_Test: 15.18 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 80.8705 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.51 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.75 - F1-Score_Train: 99.75 - Precision_Test: 19.06 - Recall_Test: 86.51 - AUPRC_Test: 82.35 - Accuracy_Test: 99.36 - F1-Score_Test: 31.23 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.17 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.58 - F1-Score_Train: 99.58 - Precision_Test: 13.73 - Recall_Test: 89.68 - AUPRC_Test: 76.92 - Accuracy_Test: 99.03 - F1-Score_Test: 23.81 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:35:17,088] Trial 15 finished with value: 81.09874115491728 and parameters: {'learning_rate': 0.03530942079438228, 'max_depth': 5, 'n_estimators': 295, 'scale_pos_weight': 9.173936003128407}. Best is trial 11 with value: 82.58523136831417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.47 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.73 - F1-Score_Train: 99.73 - Precision_Test: 19.82 - Recall_Test: 87.30 - AUPRC_Test: 84.02 - Accuracy_Test: 99.38 - F1-Score_Test: 32.31 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 81.0987 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.94 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 98.95 - F1-Score_Train: 98.96 - Precision_Test: 6.19 - Recall_Test: 87.30 - AUPRC_Test: 81.91 - Accuracy_Test: 97.75 - F1-Score_Test: 11.55 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.41 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 98.67 - F1-Score_Train: 98.69 - Precision_Test: 5.53 - Recall_Test: 93.65 - AUPRC_Test: 73.88 - Accuracy_Test: 97.30 - F1-Score_Test: 10.44 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:36:09,947] Trial 16 finished with value: 78.45864216500667 and parameters: {'learning_rate': 0.02120983226026619, 'max_depth': 5, 'n_estimators': 240, 'scale_pos_weight': 6.661848471397413}. Best is trial 11 with value: 82.58523136831417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.40 - Recall_Train: 99.96 - AUPRC_Train: 99.96 - Accuracy_Train: 98.65 - F1-Score_Train: 98.67 - Precision_Test: 5.33 - Recall_Test: 90.48 - AUPRC_Test: 79.59 - Accuracy_Test: 97.28 - F1-Score_Test: 10.08 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 78.4586 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.61 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.80 - F1-Score_Train: 99.80 - Precision_Test: 21.17 - Recall_Test: 86.51 - AUPRC_Test: 82.19 - Accuracy_Test: 99.44 - F1-Score_Test: 34.01 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.38 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.69 - F1-Score_Train: 99.69 - Precision_Test: 16.97 - Recall_Test: 88.10 - AUPRC_Test: 81.51 - Accuracy_Test: 99.26 - F1-Score_Test: 28.46 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:37:11,877] Trial 17 finished with value: 82.43156920418862 and parameters: {'learning_rate': 0.04215820386765781, 'max_depth': 5, 'n_estimators': 267, 'scale_pos_weight': 9.240204207303538}. Best is trial 11 with value: 82.58523136831417.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.59 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.79 - F1-Score_Train: 99.79 - Precision_Test: 23.21 - Recall_Test: 87.30 - AUPRC_Test: 83.59 - Accuracy_Test: 99.49 - F1-Score_Test: 36.67 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 82.4316 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.86 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.93 - F1-Score_Train: 99.93 - Precision_Test: 34.64 - Recall_Test: 84.13 - AUPRC_Test: 82.78 - Accuracy_Test: 99.71 - F1-Score_Test: 49.07 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.76 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.88 - F1-Score_Train: 99.88 - Precision_Test: 30.36 - Recall_Test: 86.51 - AUPRC_Test: 82.66 - Accuracy_Test: 99.64 - F1-Score_Test: 44.95 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:38:28,562] Trial 18 finished with value: 83.52911606459935 and parameters: {'learning_rate': 0.04229951808738263, 'max_depth': 6, 'n_estimators': 264, 'scale_pos_weight': 9.29917634419246}. Best is trial 18 with value: 83.52911606459935.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.84 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.92 - F1-Score_Train: 99.92 - Precision_Test: 39.57 - Recall_Test: 87.30 - AUPRC_Test: 85.14 - Accuracy_Test: 99.75 - F1-Score_Test: 54.46 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 83.5291 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 62.13 - Recall_Test: 83.33 - AUPRC_Test: 83.42 - Accuracy_Test: 99.89 - F1-Score_Test: 71.19 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 60.23 - Recall_Test: 84.13 - AUPRC_Test: 84.19 - Accuracy_Test: 99.88 - F1-Score_Test: 70.20 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:39:26,023] Trial 19 finished with value: 84.49389845137142 and parameters: {'learning_rate': 0.08279316655582296, 'max_depth': 6, 'n_estimators': 229, 'scale_pos_weight': 8.602804804198408}. Best is trial 19 with value: 84.49389845137142.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 66.26 - Recall_Test: 85.71 - AUPRC_Test: 85.87 - Accuracy_Test: 99.90 - F1-Score_Test: 74.74 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.4939 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 66.67 - Recall_Test: 82.54 - AUPRC_Test: 83.33 - Accuracy_Test: 99.90 - F1-Score_Test: 73.76 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 60.00 - Recall_Test: 83.33 - AUPRC_Test: 84.26 - Accuracy_Test: 99.88 - F1-Score_Test: 69.77 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:40:23,576] Trial 20 finished with value: 84.3083858957213 and parameters: {'learning_rate': 0.09424993118744852, 'max_depth': 6, 'n_estimators': 219, 'scale_pos_weight': 11.78327088422818}. Best is trial 19 with value: 84.49389845137142.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 66.05 - Recall_Test: 84.92 - AUPRC_Test: 85.33 - Accuracy_Test: 99.90 - F1-Score_Test: 74.31 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.3084 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 63.25 - Recall_Test: 83.33 - AUPRC_Test: 83.30 - Accuracy_Test: 99.89 - F1-Score_Test: 71.92 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 59.32 - Recall_Test: 83.33 - AUPRC_Test: 84.47 - Accuracy_Test: 99.88 - F1-Score_Test: 69.31 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:41:21,092] Trial 21 finished with value: 84.47521530220713 and parameters: {'learning_rate': 0.09408982015895889, 'max_depth': 6, 'n_estimators': 220, 'scale_pos_weight': 12.145834253021942}. Best is trial 19 with value: 84.49389845137142.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 69.03 - Recall_Test: 84.92 - AUPRC_Test: 85.66 - Accuracy_Test: 99.91 - F1-Score_Test: 76.16 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.4752 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 67.74 - Recall_Test: 83.33 - AUPRC_Test: 83.32 - Accuracy_Test: 99.91 - F1-Score_Test: 74.73 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 61.18 - Recall_Test: 82.54 - AUPRC_Test: 84.81 - Accuracy_Test: 99.88 - F1-Score_Test: 70.27 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:42:18,125] Trial 22 finished with value: 84.6491098397098 and parameters: {'learning_rate': 0.0997955498264482, 'max_depth': 6, 'n_estimators': 218, 'scale_pos_weight': 12.026471554732682}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 68.15 - Recall_Test: 84.92 - AUPRC_Test: 85.82 - Accuracy_Test: 99.91 - F1-Score_Test: 75.62 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.6491 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.97 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 55.32 - Recall_Test: 82.54 - AUPRC_Test: 82.72 - Accuracy_Test: 99.86 - F1-Score_Test: 66.24 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.94 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.97 - F1-Score_Train: 99.97 - Precision_Test: 47.16 - Recall_Test: 85.71 - AUPRC_Test: 84.23 - Accuracy_Test: 99.81 - F1-Score_Test: 60.85 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:43:14,673] Trial 23 finished with value: 84.11935425988526 and parameters: {'learning_rate': 0.07967057980924105, 'max_depth': 6, 'n_estimators': 202, 'scale_pos_weight': 13.550715490063864}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.96 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 56.32 - Recall_Test: 84.92 - AUPRC_Test: 85.40 - Accuracy_Test: 99.86 - F1-Score_Test: 67.72 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.1194 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 60.00 - Recall_Test: 83.33 - AUPRC_Test: 83.41 - Accuracy_Test: 99.88 - F1-Score_Test: 69.77 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.97 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 56.91 - Recall_Test: 84.92 - AUPRC_Test: 84.71 - Accuracy_Test: 99.87 - F1-Score_Test: 68.15 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:44:11,968] Trial 24 finished with value: 84.39839420450207 and parameters: {'learning_rate': 0.08276801762477547, 'max_depth': 6, 'n_estimators': 227, 'scale_pos_weight': 13.320401010709293}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 61.85 - Recall_Test: 84.92 - AUPRC_Test: 85.07 - Accuracy_Test: 99.89 - F1-Score_Test: 71.57 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.3984 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.96 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 52.76 - Recall_Test: 83.33 - AUPRC_Test: 81.67 - Accuracy_Test: 99.85 - F1-Score_Test: 64.62 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.93 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.97 - F1-Score_Train: 99.97 - Precision_Test: 47.75 - Recall_Test: 84.13 - AUPRC_Test: 84.16 - Accuracy_Test: 99.82 - F1-Score_Test: 60.92 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:44:56,911] Trial 25 finished with value: 83.83190614992175 and parameters: {'learning_rate': 0.09757784086285472, 'max_depth': 6, 'n_estimators': 159, 'scale_pos_weight': 11.267136016109033}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.96 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 53.47 - Recall_Test: 85.71 - AUPRC_Test: 85.67 - Accuracy_Test: 99.85 - F1-Score_Test: 65.85 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 83.8319 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.90 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.95 - F1-Score_Train: 99.95 - Precision_Test: 39.47 - Recall_Test: 83.33 - AUPRC_Test: 82.23 - Accuracy_Test: 99.76 - F1-Score_Test: 53.57 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.85 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.93 - F1-Score_Train: 99.93 - Precision_Test: 37.80 - Recall_Test: 87.30 - AUPRC_Test: 83.65 - Accuracy_Test: 99.74 - F1-Score_Test: 52.76 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:45:42,896] Trial 26 finished with value: 83.41010865385493 and parameters: {'learning_rate': 0.07325877265600715, 'max_depth': 6, 'n_estimators': 170, 'scale_pos_weight': 13.281644174434579}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.89 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.95 - F1-Score_Train: 99.95 - Precision_Test: 44.03 - Recall_Test: 84.92 - AUPRC_Test: 84.35 - Accuracy_Test: 99.79 - F1-Score_Test: 57.99 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 83.4101 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.83 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.91 - F1-Score_Train: 99.91 - Precision_Test: 31.20 - Recall_Test: 84.92 - AUPRC_Test: 82.76 - Accuracy_Test: 99.66 - F1-Score_Test: 45.63 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.69 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.84 - F1-Score_Train: 99.84 - Precision_Test: 26.38 - Recall_Test: 87.30 - AUPRC_Test: 82.03 - Accuracy_Test: 99.57 - F1-Score_Test: 40.52 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:46:35,176] Trial 27 finished with value: 83.07826899142579 and parameters: {'learning_rate': 0.05386863993627405, 'max_depth': 6, 'n_estimators': 206, 'scale_pos_weight': 14.74357346250575}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.84 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.92 - F1-Score_Train: 99.92 - Precision_Test: 37.76 - Recall_Test: 85.71 - AUPRC_Test: 84.44 - Accuracy_Test: 99.74 - F1-Score_Test: 52.43 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 83.0783 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 67.74 - Recall_Test: 83.33 - AUPRC_Test: 83.65 - Accuracy_Test: 99.91 - F1-Score_Test: 74.73 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 64.02 - Recall_Test: 83.33 - AUPRC_Test: 85.01 - Accuracy_Test: 99.89 - F1-Score_Test: 72.41 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:47:37,177] Trial 28 finished with value: 84.6309542107084 and parameters: {'learning_rate': 0.09987853237607923, 'max_depth': 6, 'n_estimators': 229, 'scale_pos_weight': 12.674759843282501}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 65.64 - Recall_Test: 84.92 - AUPRC_Test: 85.24 - Accuracy_Test: 99.90 - F1-Score_Test: 74.05 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.6310 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.50 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.24 - F1-Score_Train: 99.24 - Precision_Test: 8.38 - Recall_Test: 87.30 - AUPRC_Test: 82.58 - Accuracy_Test: 98.37 - F1-Score_Test: 15.29 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.08 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.02 - F1-Score_Train: 99.03 - Precision_Test: 7.14 - Recall_Test: 92.86 - AUPRC_Test: 76.74 - Accuracy_Test: 97.96 - F1-Score_Test: 13.27 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:48:05,109] Trial 29 finished with value: 80.71202163792437 and parameters: {'learning_rate': 0.06561064528456287, 'max_depth': 5, 'n_estimators': 107, 'scale_pos_weight': 9.950903892133745}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.40 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.19 - F1-Score_Train: 99.19 - Precision_Test: 8.15 - Recall_Test: 88.89 - AUPRC_Test: 82.82 - Accuracy_Test: 98.30 - F1-Score_Test: 14.92 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 80.7120 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.75 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.88 - F1-Score_Train: 99.88 - Precision_Test: 27.27 - Recall_Test: 85.71 - AUPRC_Test: 82.75 - Accuracy_Test: 99.59 - F1-Score_Test: 41.38 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.59 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.79 - F1-Score_Train: 99.79 - Precision_Test: 22.52 - Recall_Test: 86.51 - AUPRC_Test: 78.75 - Accuracy_Test: 99.48 - F1-Score_Test: 35.74 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:48:40,583] Trial 30 finished with value: 81.98502239664322 and parameters: {'learning_rate': 0.07913928891684985, 'max_depth': 6, 'n_estimators': 123, 'scale_pos_weight': 11.253706694179165}. Best is trial 22 with value: 84.6491098397098.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.76 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.88 - F1-Score_Train: 99.88 - Precision_Test: 29.86 - Recall_Test: 86.51 - AUPRC_Test: 84.45 - Accuracy_Test: 99.64 - F1-Score_Test: 44.40 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 81.9850 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 64.81 - Recall_Test: 83.33 - AUPRC_Test: 83.81 - Accuracy_Test: 99.90 - F1-Score_Test: 72.92 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 62.50 - Recall_Test: 83.33 - AUPRC_Test: 84.74 - Accuracy_Test: 99.89 - F1-Score_Test: 71.43 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:49:38,224] Trial 31 finished with value: 84.72200668322499 and parameters: {'learning_rate': 0.096389491823576, 'max_depth': 6, 'n_estimators': 229, 'scale_pos_weight': 12.927141498539687}. Best is trial 31 with value: 84.72200668322499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 69.03 - Recall_Test: 84.92 - AUPRC_Test: 85.62 - Accuracy_Test: 99.91 - F1-Score_Test: 76.16 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.7220 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 61.76 - Recall_Test: 83.33 - AUPRC_Test: 83.58 - Accuracy_Test: 99.89 - F1-Score_Test: 70.95 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.97 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 58.56 - Recall_Test: 84.13 - AUPRC_Test: 84.42 - Accuracy_Test: 99.87 - F1-Score_Test: 69.06 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:50:36,046] Trial 32 finished with value: 84.41202250085115 and parameters: {'learning_rate': 0.08330260705016251, 'max_depth': 6, 'n_estimators': 230, 'scale_pos_weight': 12.830432435686129}. Best is trial 31 with value: 84.72200668322499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 62.94 - Recall_Test: 84.92 - AUPRC_Test: 85.24 - Accuracy_Test: 99.89 - F1-Score_Test: 72.30 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.4120 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.92 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.96 - F1-Score_Train: 99.96 - Precision_Test: 41.73 - Recall_Test: 84.13 - AUPRC_Test: 83.02 - Accuracy_Test: 99.78 - F1-Score_Test: 55.79 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.85 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.92 - F1-Score_Train: 99.92 - Precision_Test: 36.49 - Recall_Test: 85.71 - AUPRC_Test: 83.40 - Accuracy_Test: 99.72 - F1-Score_Test: 51.18 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:51:26,327] Trial 33 finished with value: 83.83608566796094 and parameters: {'learning_rate': 0.06806205549222889, 'max_depth': 6, 'n_estimators': 195, 'scale_pos_weight': 13.761935596255714}. Best is trial 31 with value: 84.72200668322499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.91 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.95 - F1-Score_Train: 99.95 - Precision_Test: 45.38 - Recall_Test: 85.71 - AUPRC_Test: 85.09 - Accuracy_Test: 99.80 - F1-Score_Test: 59.34 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 83.8361 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 69.54 - Recall_Test: 83.33 - AUPRC_Test: 83.53 - Accuracy_Test: 99.91 - F1-Score_Test: 75.81 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 64.42 - Recall_Test: 83.33 - AUPRC_Test: 85.11 - Accuracy_Test: 99.89 - F1-Score_Test: 72.66 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:52:34,256] Trial 34 finished with value: 84.5850001188499 and parameters: {'learning_rate': 0.09757508786165124, 'max_depth': 6, 'n_estimators': 255, 'scale_pos_weight': 14.256821641418599}. Best is trial 31 with value: 84.72200668322499.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 71.33 - Recall_Test: 84.92 - AUPRC_Test: 85.12 - Accuracy_Test: 99.92 - F1-Score_Test: 77.54 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.5850 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 67.97 - Recall_Test: 82.54 - AUPRC_Test: 83.65 - Accuracy_Test: 99.91 - F1-Score_Test: 74.55 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 64.60 - Recall_Test: 82.54 - AUPRC_Test: 85.21 - Accuracy_Test: 99.89 - F1-Score_Test: 72.47 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:53:40,084] Trial 35 finished with value: 84.80254817584363 and parameters: {'learning_rate': 0.09769777927597675, 'max_depth': 6, 'n_estimators': 255, 'scale_pos_weight': 14.218608915998557}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 71.33 - Recall_Test: 84.92 - AUPRC_Test: 85.54 - Accuracy_Test: 99.92 - F1-Score_Test: 77.54 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.8025 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.35 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.16 - F1-Score_Train: 99.17 - Precision_Test: 7.93 - Recall_Test: 88.89 - AUPRC_Test: 80.26 - Accuracy_Test: 98.25 - F1-Score_Test: 14.56 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.57 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 98.75 - F1-Score_Train: 98.77 - Precision_Test: 5.95 - Recall_Test: 92.86 - AUPRC_Test: 75.82 - Accuracy_Test: 97.52 - F1-Score_Test: 11.19 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:54:17,981] Trial 36 finished with value: 77.94933183673454 and parameters: {'learning_rate': 0.06049671902357879, 'max_depth': 3, 'n_estimators': 278, 'scale_pos_weight': 12.929367685034475}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.15 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.06 - F1-Score_Train: 99.07 - Precision_Test: 7.17 - Recall_Test: 87.30 - AUPRC_Test: 77.78 - Accuracy_Test: 98.08 - F1-Score_Test: 13.25 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 77.9493 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 63.64 - Recall_Test: 83.33 - AUPRC_Test: 84.06 - Accuracy_Test: 99.89 - F1-Score_Test: 72.16 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.97 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 57.30 - Recall_Test: 84.13 - AUPRC_Test: 84.61 - Accuracy_Test: 99.87 - F1-Score_Test: 68.17 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:55:18,757] Trial 37 finished with value: 84.57824750440376 and parameters: {'learning_rate': 0.09930709483939626, 'max_depth': 5, 'n_estimators': 281, 'scale_pos_weight': 14.998740583026917}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 62.21 - Recall_Test: 84.92 - AUPRC_Test: 85.07 - Accuracy_Test: 99.89 - F1-Score_Test: 71.81 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.5782 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.91 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.96 - F1-Score_Train: 99.96 - Precision_Test: 41.96 - Recall_Test: 84.92 - AUPRC_Test: 82.65 - Accuracy_Test: 99.78 - F1-Score_Test: 56.17 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.85 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.92 - F1-Score_Train: 99.92 - Precision_Test: 36.73 - Recall_Test: 85.71 - AUPRC_Test: 83.70 - Accuracy_Test: 99.73 - F1-Score_Test: 51.43 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:56:26,345] Trial 38 finished with value: 83.91690101454749 and parameters: {'learning_rate': 0.0517794151280147, 'max_depth': 6, 'n_estimators': 251, 'scale_pos_weight': 12.526156465651162}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.90 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.95 - F1-Score_Train: 99.95 - Precision_Test: 46.78 - Recall_Test: 86.51 - AUPRC_Test: 85.41 - Accuracy_Test: 99.81 - F1-Score_Test: 60.72 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 83.9169 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 56.38 - Recall_Test: 84.13 - AUPRC_Test: 83.47 - Accuracy_Test: 99.86 - F1-Score_Test: 67.52 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.94 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.97 - F1-Score_Train: 99.97 - Precision_Test: 49.52 - Recall_Test: 82.54 - AUPRC_Test: 84.27 - Accuracy_Test: 99.83 - F1-Score_Test: 61.90 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:57:26,387] Trial 39 finished with value: 84.2293339619397 and parameters: {'learning_rate': 0.07406440186743024, 'max_depth': 6, 'n_estimators': 235, 'scale_pos_weight': 14.10472687758145}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 59.12 - Recall_Test: 84.92 - AUPRC_Test: 84.94 - Accuracy_Test: 99.88 - F1-Score_Test: 69.71 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.2293 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 57.69 - Recall_Test: 83.33 - AUPRC_Test: 82.84 - Accuracy_Test: 99.87 - F1-Score_Test: 68.18 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.96 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 53.85 - Recall_Test: 83.33 - AUPRC_Test: 84.01 - Accuracy_Test: 99.85 - F1-Score_Test: 65.42 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:58:19,937] Trial 40 finished with value: 83.87110040393095 and parameters: {'learning_rate': 0.08788926803943453, 'max_depth': 6, 'n_estimators': 207, 'scale_pos_weight': 11.754962062829808}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 62.57 - Recall_Test: 84.92 - AUPRC_Test: 84.77 - Accuracy_Test: 99.89 - F1-Score_Test: 72.05 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 83.8711 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 100.00 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 65.82 - Recall_Test: 82.54 - AUPRC_Test: 83.28 - Accuracy_Test: 99.90 - F1-Score_Test: 73.24 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 62.72 - Recall_Test: 84.13 - AUPRC_Test: 85.11 - Accuracy_Test: 99.89 - F1-Score_Test: 71.86 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 13:59:23,740] Trial 41 finished with value: 84.61044405043434 and parameters: {'learning_rate': 0.08766984736408615, 'max_depth': 6, 'n_estimators': 253, 'scale_pos_weight': 14.129554192938574}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 100.00 - F1-Score_Train: 100.00 - Precision_Test: 65.64 - Recall_Test: 84.92 - AUPRC_Test: 85.44 - Accuracy_Test: 99.90 - F1-Score_Test: 74.05 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.6104 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.99 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 61.05 - Recall_Test: 83.33 - AUPRC_Test: 83.13 - Accuracy_Test: 99.88 - F1-Score_Test: 70.47 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.97 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 55.50 - Recall_Test: 84.13 - AUPRC_Test: 85.24 - Accuracy_Test: 99.86 - F1-Score_Test: 66.88 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 14:00:31,532] Trial 42 finished with value: 84.70247435489021 and parameters: {'learning_rate': 0.07268320017950446, 'max_depth': 6, 'n_estimators': 259, 'scale_pos_weight': 13.808745386482586}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 64.85 - Recall_Test: 84.92 - AUPRC_Test: 85.73 - Accuracy_Test: 99.90 - F1-Score_Test: 73.54 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.7025 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.97 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 57.07 - Recall_Test: 83.33 - AUPRC_Test: 83.67 - Accuracy_Test: 99.87 - F1-Score_Test: 67.74 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.95 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.97 - F1-Score_Train: 99.97 - Precision_Test: 49.77 - Recall_Test: 85.71 - AUPRC_Test: 84.74 - Accuracy_Test: 99.83 - F1-Score_Test: 62.97 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 14:01:44,808] Trial 43 finished with value: 84.3940267654455 and parameters: {'learning_rate': 0.06115769628397858, 'max_depth': 6, 'n_estimators': 274, 'scale_pos_weight': 12.97679547241286}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.97 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 56.54 - Recall_Test: 85.71 - AUPRC_Test: 84.78 - Accuracy_Test: 99.87 - F1-Score_Test: 68.14 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.3940 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.95 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 51.46 - Recall_Test: 84.13 - AUPRC_Test: 83.08 - Accuracy_Test: 99.84 - F1-Score_Test: 63.86 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.94 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.97 - F1-Score_Train: 99.97 - Precision_Test: 46.72 - Recall_Test: 84.92 - AUPRC_Test: 84.33 - Accuracy_Test: 99.81 - F1-Score_Test: 60.28 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 14:02:41,489] Trial 44 finished with value: 84.24202608093834 and parameters: {'learning_rate': 0.07382979755866753, 'max_depth': 6, 'n_estimators': 214, 'scale_pos_weight': 12.486802872834668}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.96 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 56.25 - Recall_Test: 85.71 - AUPRC_Test: 85.32 - Accuracy_Test: 99.86 - F1-Score_Test: 67.92 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.2420 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.61 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.80 - F1-Score_Train: 99.80 - Precision_Test: 21.78 - Recall_Test: 87.30 - AUPRC_Test: 81.10 - Accuracy_Test: 99.45 - F1-Score_Test: 34.87 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.24 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.62 - F1-Score_Train: 99.62 - Precision_Test: 14.91 - Recall_Test: 89.68 - AUPRC_Test: 80.62 - Accuracy_Test: 99.12 - F1-Score_Test: 25.57 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 14:03:28,834] Trial 45 finished with value: 81.84253600082046 and parameters: {'learning_rate': 0.06800639120007515, 'max_depth': 4, 'n_estimators': 255, 'scale_pos_weight': 13.72211105327384}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.43 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.71 - F1-Score_Train: 99.71 - Precision_Test: 18.21 - Recall_Test: 87.30 - AUPRC_Test: 83.81 - Accuracy_Test: 99.32 - F1-Score_Test: 30.14 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 81.8425 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.91 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.95 - F1-Score_Train: 99.95 - Precision_Test: 43.09 - Recall_Test: 84.13 - AUPRC_Test: 82.89 - Accuracy_Test: 99.79 - F1-Score_Test: 56.99 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.83 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.92 - F1-Score_Train: 99.92 - Precision_Test: 34.84 - Recall_Test: 85.71 - AUPRC_Test: 83.79 - Accuracy_Test: 99.71 - F1-Score_Test: 49.54 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 14:04:13,718] Trial 46 finished with value: 83.74850152967751 and parameters: {'learning_rate': 0.09032390378027735, 'max_depth': 5, 'n_estimators': 195, 'scale_pos_weight': 12.06945089918033}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.87 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.93 - F1-Score_Train: 99.93 - Precision_Test: 40.38 - Recall_Test: 84.92 - AUPRC_Test: 84.57 - Accuracy_Test: 99.76 - F1-Score_Test: 54.73 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 83.7485 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 57.38 - Recall_Test: 83.33 - AUPRC_Test: 82.15 - Accuracy_Test: 99.87 - F1-Score_Test: 67.96 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.96 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 54.08 - Recall_Test: 84.13 - AUPRC_Test: 84.51 - Accuracy_Test: 99.85 - F1-Score_Test: 65.84 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 14:05:11,505] Trial 47 finished with value: 84.02640705477707 and parameters: {'learning_rate': 0.07820734925275324, 'max_depth': 6, 'n_estimators': 224, 'scale_pos_weight': 11.07498033580423}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 62.79 - Recall_Test: 85.71 - AUPRC_Test: 85.41 - Accuracy_Test: 99.89 - F1-Score_Test: 72.48 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.0264 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.98 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 59.09 - Recall_Test: 82.54 - AUPRC_Test: 83.31 - Accuracy_Test: 99.87 - F1-Score_Test: 68.87 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.96 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.98 - F1-Score_Train: 99.98 - Precision_Test: 53.89 - Recall_Test: 82.54 - AUPRC_Test: 83.88 - Accuracy_Test: 99.85 - F1-Score_Test: 65.20 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 14:06:11,631] Trial 48 finished with value: 84.14956191958008 and parameters: {'learning_rate': 0.0999907772460011, 'max_depth': 5, 'n_estimators': 243, 'scale_pos_weight': 10.3224165372168}. Best is trial 35 with value: 84.80254817584363.
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.97 - Recall_Train: 100.00 - AUPRC_Train: 100.00 - Accuracy_Train: 99.99 - F1-Score_Train: 99.99 - Precision_Test: 59.78 - Recall_Test: 84.92 - AUPRC_Test: 85.26 - Accuracy_Test: 99.88 - F1-Score_Test: 70.16 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 84.1496 🔍 Optimizando hiperparámetros para XGBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.24 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 98.58 - F1-Score_Train: 98.60 - Precision_Test: 4.60 - Recall_Test: 87.30 - AUPRC_Test: 74.48 - Accuracy_Test: 96.93 - F1-Score_Test: 8.74 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (1, 22) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)... ✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 95.75 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 97.78 - F1-Score_Train: 97.83 - Precision_Test: 3.36 - Recall_Test: 93.65 - AUPRC_Test: 70.71 - Accuracy_Test: 95.46 - F1-Score_Test: 6.49 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (2, 22) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando XGBoost (Optuna con SMOTE)...
[I 2024-12-19 14:06:56,848] Trial 49 finished with value: 73.32809020739643 and parameters: {'learning_rate': 0.016815478978109078, 'max_depth': 6, 'n_estimators': 177, 'scale_pos_weight': 14.611683058636316}. Best is trial 35 with value: 84.80254817584363.
[I 2024-12-19 14:06:56,852] A new study created in memory with name: no-name-7560ed73-8ab9-4145-a9fd-63e8e7794e9c
✅ Resultados para XGBoost (Optuna con SMOTE): - Modelo: XGBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 96.47 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 98.17 - F1-Score_Train: 98.20 - Precision_Test: 3.92 - Recall_Test: 92.06 - AUPRC_Test: 74.80 - Accuracy_Test: 96.19 - F1-Score_Test: 7.51 - objective: None - kwargs: None ✅ Tamaño del DataFrame actualizado: (3, 22) 🏆 Promedio de AUPRC en validación cruzada: 73.3281 🚀 Optimización de Hiperparámetros con Optuna para CatBoost... 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4850805 total: 163ms remaining: 22.7s 1: learn: 0.3488096 total: 303ms remaining: 20.9s 2: learn: 0.2702077 total: 417ms remaining: 19s 3: learn: 0.2051009 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para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.14 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 99.56 - F1-Score_Train: 99.57 - Precision_Test: 12.91 - Recall_Test: 87.30 - AUPRC_Test: 72.07 - Accuracy_Test: 98.99 - F1-Score_Test: 22.49 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 140 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4987237 total: 134ms remaining: 18.6s 1: learn: 0.3753171 total: 270ms remaining: 18.6s 2: learn: 0.2836961 total: 400ms remaining: 18.3s 3: learn: 0.2318818 total: 543ms remaining: 18.5s 4: learn: 0.1883658 total: 686ms remaining: 18.5s 5: learn: 0.1727098 total: 839ms remaining: 18.7s 6: learn: 0.1591220 total: 985ms remaining: 18.7s 7: learn: 0.1438954 total: 1.13s remaining: 18.6s 8: learn: 0.1356891 total: 1.27s remaining: 18.5s 9: learn: 0.1272846 total: 1.41s remaining: 18.4s 10: learn: 0.1184757 total: 1.57s remaining: 18.4s 11: learn: 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11.1s remaining: 944ms 129: learn: 0.0137801 total: 11.1s remaining: 856ms 130: learn: 0.0135596 total: 11.2s remaining: 771ms 131: learn: 0.0134060 total: 11.3s remaining: 685ms 132: learn: 0.0132186 total: 11.4s remaining: 598ms 133: learn: 0.0130907 total: 11.4s remaining: 512ms 134: learn: 0.0129283 total: 11.5s remaining: 426ms 135: learn: 0.0128073 total: 11.6s remaining: 341ms 136: learn: 0.0127178 total: 11.7s remaining: 255ms 137: learn: 0.0125939 total: 11.7s remaining: 170ms 138: learn: 0.0124044 total: 11.8s remaining: 84.8ms 139: learn: 0.0123299 total: 11.9s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.75 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.37 - F1-Score_Train: 99.37 - Precision_Test: 10.31 - Recall_Test: 89.68 - AUPRC_Test: 71.41 - Accuracy_Test: 98.67 - F1-Score_Test: 18.49 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 140 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: 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[I 2024-12-19 14:07:40,213] Trial 0 finished with value: 71.43498362547278 and parameters: {'learning_rate': 0.09972096837965244, 'max_depth': 4, 'n_estimators': 140, 'scale_pos_weight': 5.488005039321137}. Best is trial 0 with value: 71.43498362547278.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.80 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 99.39 - F1-Score_Train: 99.40 - Precision_Test: 10.49 - Recall_Test: 88.10 - AUPRC_Test: 70.83 - Accuracy_Test: 98.72 - F1-Score_Test: 18.75 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 140 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 71.4350 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6409413 total: 74.2ms remaining: 16.5s 1: learn: 0.5917553 total: 145ms remaining: 16.1s 2: learn: 0.5477579 total: 263ms remaining: 19.4s 3: learn: 0.5033319 total: 399ms remaining: 21.9s 4: learn: 0.4704481 total: 535ms remaining: 23.4s 5: learn: 0.4348400 total: 668ms remaining: 24.3s 6: learn: 0.3999592 total: 807ms remaining: 25s 7: learn: 0.3683739 total: 959ms remaining: 25.9s 8: learn: 0.3433553 total: 1.1s 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21.4s remaining: 96.2ms 223: learn: 0.0214158 total: 21.5s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 95.24 - Recall_Train: 100.00 - AUPRC_Train: 99.87 - Accuracy_Train: 97.50 - F1-Score_Train: 97.56 - Precision_Test: 2.92 - Recall_Test: 91.27 - AUPRC_Test: 64.96 - Accuracy_Test: 94.87 - F1-Score_Test: 5.65 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 224 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.17 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6455346 total: 73.1ms remaining: 16.3s 1: learn: 0.6049447 total: 139ms remaining: 15.4s 2: learn: 0.5617661 total: 209ms remaining: 15.4s 3: learn: 0.5135602 total: 285ms remaining: 15.7s 4: learn: 0.4830378 total: 356ms remaining: 15.6s 5: learn: 0.4600783 total: 423ms remaining: 15.4s 6: learn: 0.4345363 total: 520ms remaining: 16.1s 7: learn: 0.4077903 total: 595ms remaining: 16.1s 8: learn: 0.3821730 total: 674ms remaining: 16.1s 9: learn: 0.3603296 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19.3s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 93.23 - Recall_Train: 100.00 - AUPRC_Train: 99.81 - Accuracy_Train: 96.37 - F1-Score_Train: 96.50 - Precision_Test: 2.21 - Recall_Test: 96.83 - AUPRC_Test: 63.73 - Accuracy_Test: 92.79 - F1-Score_Test: 4.32 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 224 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.17 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6469155 total: 68.6ms remaining: 15.3s 1: learn: 0.5871850 total: 144ms remaining: 16s 2: learn: 0.5346526 total: 214ms remaining: 15.7s 3: learn: 0.4977070 total: 283ms remaining: 15.5s 4: learn: 0.4743074 total: 349ms remaining: 15.3s 5: learn: 0.4426596 total: 415ms remaining: 15.1s 6: learn: 0.4111594 total: 493ms remaining: 15.3s 7: learn: 0.3886754 total: 609ms remaining: 16.4s 8: learn: 0.3585350 total: 734ms remaining: 17.5s 9: learn: 0.3345930 total: 872ms remaining: 18.7s 10: learn: 0.3143735 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[I 2024-12-19 14:08:49,372] Trial 1 finished with value: 64.77081821400259 and parameters: {'learning_rate': 0.019464969464643017, 'max_depth': 4, 'n_estimators': 224, 'scale_pos_weight': 10.167674687462965}. Best is trial 0 with value: 71.43498362547278.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 94.60 - Recall_Train: 100.00 - AUPRC_Train: 99.81 - Accuracy_Train: 97.14 - F1-Score_Train: 97.22 - Precision_Test: 2.57 - Recall_Test: 90.48 - AUPRC_Test: 65.63 - Accuracy_Test: 94.22 - F1-Score_Test: 5.00 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 224 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.17 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 64.7708 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5916089 total: 80.1ms remaining: 16.2s 1: learn: 0.5085394 total: 160ms remaining: 16.1s 2: learn: 0.4309394 total: 242ms remaining: 16.1s 3: learn: 0.3728776 total: 333ms remaining: 16.6s 4: learn: 0.3158271 total: 414ms remaining: 16.4s 5: learn: 0.2710321 total: 504ms remaining: 16.5s 6: learn: 0.2298193 total: 596ms remaining: 16.7s 7: learn: 0.2084492 total: 675ms remaining: 16.5s 8: learn: 0.1848003 total: 757ms 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✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.37 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 99.17 - F1-Score_Train: 99.18 - Precision_Test: 7.89 - Recall_Test: 88.10 - AUPRC_Test: 68.46 - Accuracy_Test: 98.25 - F1-Score_Test: 14.48 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 203 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.79 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5956403 total: 80.6ms remaining: 16.3s 1: learn: 0.5153592 total: 153ms remaining: 15.4s 2: learn: 0.4383207 total: 236ms remaining: 15.7s 3: learn: 0.3812338 total: 338ms remaining: 16.8s 4: learn: 0.3344227 total: 417ms remaining: 16.5s 5: learn: 0.2896466 total: 502ms remaining: 16.5s 6: learn: 0.2639259 total: 595ms remaining: 16.6s 7: learn: 0.2354605 total: 681ms remaining: 16.6s 8: learn: 0.2135856 total: 768ms remaining: 16.6s 9: learn: 0.2023229 total: 851ms remaining: 16.4s 10: learn: 0.1870929 total: 925ms 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Precision_Train: 97.87 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 98.91 - F1-Score_Train: 98.92 - Precision_Test: 6.65 - Recall_Test: 92.86 - AUPRC_Test: 70.92 - Accuracy_Test: 97.80 - F1-Score_Test: 12.41 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 203 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.79 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5960978 total: 74.5ms remaining: 15.1s 1: learn: 0.5047371 total: 150ms remaining: 15.1s 2: learn: 0.4531846 total: 220ms remaining: 14.7s 3: learn: 0.3908628 total: 305ms remaining: 15.2s 4: learn: 0.3403072 total: 384ms remaining: 15.2s 5: learn: 0.2913329 total: 466ms remaining: 15.3s 6: learn: 0.2611673 total: 554ms remaining: 15.5s 7: learn: 0.2326883 total: 632ms remaining: 15.4s 8: learn: 0.2111890 total: 711ms remaining: 15.3s 9: learn: 0.1909593 total: 796ms remaining: 15.4s 10: learn: 0.1768920 total: 867ms remaining: 15.1s 11: learn: 0.1629039 total: 942ms remaining: 15s 12: learn: 0.1477986 total: 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[I 2024-12-19 14:09:55,420] Trial 2 finished with value: 70.54737699484406 and parameters: {'learning_rate': 0.03777341007132233, 'max_depth': 5, 'n_estimators': 203, 'scale_pos_weight': 9.790762307668434}. Best is trial 0 with value: 71.43498362547278.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.93 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 98.95 - F1-Score_Train: 98.96 - Precision_Test: 6.46 - Recall_Test: 88.89 - AUPRC_Test: 72.27 - Accuracy_Test: 97.81 - F1-Score_Test: 12.04 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 203 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.79 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 70.5474 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6611628 total: 94.4ms remaining: 27.4s 1: learn: 0.6293344 total: 180ms remaining: 26s 2: learn: 0.6010530 total: 271ms remaining: 26s 3: learn: 0.5667043 total: 376ms remaining: 26.9s 4: learn: 0.5324599 total: 486ms remaining: 27.8s 5: learn: 0.5074492 total: 574ms remaining: 27.3s 6: learn: 0.4807116 total: 685ms remaining: 27.8s 7: learn: 0.4577233 total: 811ms remaining: 28.7s 8: learn: 0.4322893 total: 958ms 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mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 291 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 13.74 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6594691 total: 157ms remaining: 45.7s 1: 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Sobreajuste: 1 - Precision_Train: 95.09 - Recall_Train: 100.00 - AUPRC_Train: 99.93 - Accuracy_Train: 97.42 - F1-Score_Train: 97.48 - Precision_Test: 3.07 - Recall_Test: 96.83 - AUPRC_Test: 70.08 - Accuracy_Test: 94.85 - F1-Score_Test: 5.95 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 291 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 13.74 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6609791 total: 95.7ms remaining: 27.8s 1: learn: 0.6211871 total: 183ms remaining: 26.5s 2: learn: 0.5951111 total: 275ms remaining: 26.4s 3: learn: 0.5659838 total: 370ms remaining: 26.5s 4: learn: 0.5385403 total: 454ms remaining: 25.9s 5: learn: 0.5075492 total: 545ms remaining: 25.9s 6: learn: 0.4828915 total: 642ms remaining: 26.1s 7: learn: 0.4658243 total: 732ms remaining: 25.9s 8: learn: 0.4397027 total: 823ms remaining: 25.8s 9: learn: 0.4192101 total: 921ms remaining: 25.9s 10: learn: 0.3964095 total: 1.01s remaining: 25.7s 11: learn: 0.3797722 total: 1.11s remaining: 25.7s 12: learn: 0.3627149 total: 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[I 2024-12-19 14:11:43,682] Trial 3 finished with value: 70.27439832653555 and parameters: {'learning_rate': 0.011484619114104772, 'max_depth': 6, 'n_estimators': 291, 'scale_pos_weight': 13.73854959933593}. Best is trial 0 with value: 71.43498362547278.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 96.00 - Recall_Train: 100.00 - AUPRC_Train: 99.91 - Accuracy_Train: 97.92 - F1-Score_Train: 97.96 - Precision_Test: 3.47 - Recall_Test: 90.48 - AUPRC_Test: 68.82 - Accuracy_Test: 95.75 - F1-Score_Test: 6.68 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 291 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 13.74 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 70.2744 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4831325 total: 88ms remaining: 15.6s 1: learn: 0.3218375 total: 179ms remaining: 15.7s 2: learn: 0.2252414 total: 267ms remaining: 15.6s 3: learn: 0.1737974 total: 362ms remaining: 15.8s 4: learn: 0.1282940 total: 459ms remaining: 15.9s 5: learn: 0.1061082 total: 542ms remaining: 15.5s 6: learn: 0.0856105 total: 640ms remaining: 15.6s 7: learn: 0.0759760 total: 732ms remaining: 15.6s 8: learn: 0.0641582 total: 830ms 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Accuracy_Test: 99.35 - F1-Score_Test: 31.36 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 178 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.22 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 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19.7s remaining: 568ms 173: learn: 0.0032651 total: 19.7s remaining: 454ms 174: learn: 0.0032651 total: 19.8s remaining: 339ms 175: learn: 0.0032650 total: 19.9s remaining: 226ms 176: learn: 0.0032651 total: 20s remaining: 113ms 177: learn: 0.0032651 total: 20s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.38 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.69 - F1-Score_Train: 99.69 - Precision_Test: 19.24 - Recall_Test: 88.89 - AUPRC_Test: 72.98 - Accuracy_Test: 99.35 - F1-Score_Test: 31.64 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 178 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.22 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4793532 total: 83.3ms remaining: 14.8s 1: learn: 0.3044493 total: 175ms remaining: 15.4s 2: learn: 0.2039520 total: 273ms remaining: 15.9s 3: learn: 0.1535865 total: 367ms remaining: 16s 4: learn: 0.1203180 total: 457ms remaining: 15.8s 5: learn: 0.1045675 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[I 2024-12-19 14:12:47,050] Trial 4 finished with value: 76.71888513333191 and parameters: {'learning_rate': 0.09180002730546635, 'max_depth': 6, 'n_estimators': 178, 'scale_pos_weight': 11.21811114252904}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.56 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.78 - F1-Score_Train: 99.78 - Precision_Test: 21.28 - Recall_Test: 87.30 - AUPRC_Test: 79.09 - Accuracy_Test: 99.44 - F1-Score_Test: 34.21 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 178 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.22 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 76.7189 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5626407 total: 108ms remaining: 19.9s 1: learn: 0.4479412 total: 201ms remaining: 18.4s 2: learn: 0.3468387 total: 286ms remaining: 17.4s 3: learn: 0.2662888 total: 397ms remaining: 17.9s 4: learn: 0.2112297 total: 493ms remaining: 17.7s 5: learn: 0.1839046 total: 584ms remaining: 17.4s 6: learn: 0.1489903 total: 688ms remaining: 17.5s 7: learn: 0.1305856 total: 777ms remaining: 17.2s 8: learn: 0.1143243 total: 870ms 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0.0032992 total: 21.8s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.13 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.56 - F1-Score_Train: 99.57 - Precision_Test: 12.92 - Recall_Test: 88.89 - AUPRC_Test: 75.69 - Accuracy_Test: 98.97 - F1-Score_Test: 22.56 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 185 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 13.38 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5556976 total: 85.9ms remaining: 15.8s 1: learn: 0.4490215 total: 188ms remaining: 17.2s 2: learn: 0.3730075 total: 276ms remaining: 16.7s 3: learn: 0.3056643 total: 370ms remaining: 16.7s 4: learn: 0.2513444 total: 479ms remaining: 17.2s 5: learn: 0.2265902 total: 558ms remaining: 16.6s 6: learn: 0.2015552 total: 654ms remaining: 16.6s 7: learn: 0.1649524 total: 747ms remaining: 16.5s 8: learn: 0.1445993 total: 849ms remaining: 16.6s 9: learn: 0.1328449 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0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.84 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.41 - F1-Score_Train: 99.42 - Precision_Test: 10.83 - Recall_Test: 90.48 - AUPRC_Test: 65.02 - Accuracy_Test: 98.73 - F1-Score_Test: 19.34 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 185 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 13.38 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5622818 total: 81ms remaining: 14.9s 1: learn: 0.4309370 total: 172ms remaining: 15.8s 2: learn: 0.3451481 total: 287ms remaining: 17.4s 3: learn: 0.2777314 total: 388ms remaining: 17.5s 4: learn: 0.2197595 total: 480ms remaining: 17.3s 5: learn: 0.1797786 total: 579ms remaining: 17.3s 6: learn: 0.1493524 total: 678ms remaining: 17.2s 7: learn: 0.1302513 total: 767ms remaining: 17s 8: learn: 0.1164816 total: 865ms remaining: 16.9s 9: learn: 0.1022141 total: 951ms remaining: 16.6s 10: learn: 0.0941701 total: 1.04s remaining: 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[I 2024-12-19 14:13:57,003] Trial 5 finished with value: 72.089155485785 and parameters: {'learning_rate': 0.050682454663907056, 'max_depth': 6, 'n_estimators': 185, 'scale_pos_weight': 13.37714459399436}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.89 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.44 - F1-Score_Train: 99.44 - Precision_Test: 11.07 - Recall_Test: 87.30 - AUPRC_Test: 75.56 - Accuracy_Test: 98.80 - F1-Score_Test: 19.64 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 185 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 13.38 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 72.0892 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6683742 total: 69.1ms remaining: 18.9s 1: learn: 0.6424842 total: 155ms remaining: 21.2s 2: learn: 0.6174096 total: 226ms remaining: 20.5s 3: learn: 0.5894397 total: 310ms remaining: 21s 4: learn: 0.5623418 total: 384ms remaining: 20.7s 5: learn: 0.5426693 total: 461ms remaining: 20.7s 6: learn: 0.5221119 total: 544ms remaining: 20.8s 7: learn: 0.5048724 total: 619ms remaining: 20.6s 8: learn: 0.4847866 total: 692ms 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66.55 - Accuracy_Test: 94.31 - F1-Score_Test: 5.12 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 275 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.79 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 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allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 275 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.79 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6699194 total: 70.8ms remaining: 19.4s 1: learn: 0.6502111 total: 133ms remaining: 18.1s 2: learn: 0.6216282 total: 201ms remaining: 18.3s 3: learn: 0.6050647 total: 269ms remaining: 18.2s 4: learn: 0.5882724 total: 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[I 2024-12-19 14:15:21,101] Trial 6 finished with value: 65.7095697060385 and parameters: {'learning_rate': 0.010206730170698685, 'max_depth': 4, 'n_estimators': 275, 'scale_pos_weight': 6.78843201732805}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 94.04 - Recall_Train: 99.56 - AUPRC_Train: 99.74 - Accuracy_Train: 96.62 - F1-Score_Train: 96.72 - Precision_Test: 2.32 - Recall_Test: 90.48 - AUPRC_Test: 68.34 - Accuracy_Test: 93.59 - F1-Score_Test: 4.53 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 275 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.79 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 65.7096 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6607241 total: 81.8ms remaining: 9.08s 1: learn: 0.6272280 total: 160ms remaining: 8.81s 2: learn: 0.5950888 total: 239ms remaining: 8.68s 3: learn: 0.5643685 total: 334ms remaining: 9.02s 4: learn: 0.5332826 total: 415ms remaining: 8.89s 5: learn: 0.5073801 total: 496ms remaining: 8.77s 6: learn: 0.4850175 total: 581ms remaining: 8.71s 7: learn: 0.4579854 total: 664ms remaining: 8.63s 8: learn: 0.4382681 total: 767ms 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remaining: 551ms 107: learn: 0.0430507 total: 11.9s remaining: 440ms 108: learn: 0.0425845 total: 12s remaining: 329ms 109: learn: 0.0421091 total: 12.1s remaining: 219ms 110: learn: 0.0416903 total: 12.1s remaining: 109ms 111: learn: 0.0412340 total: 12.2s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 92.08 - Recall_Train: 99.99 - AUPRC_Train: 99.80 - Accuracy_Train: 95.70 - F1-Score_Train: 95.87 - Precision_Test: 1.75 - Recall_Test: 91.27 - AUPRC_Test: 68.01 - Accuracy_Test: 91.35 - F1-Score_Test: 3.43 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 112 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 12.44 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6589820 total: 94.7ms remaining: 10.5s 1: learn: 0.6244007 total: 171ms remaining: 9.43s 2: learn: 0.5906306 total: 254ms remaining: 9.24s 3: learn: 0.5650909 total: 342ms remaining: 9.22s 4: learn: 0.5366141 total: 420ms remaining: 8.98s 5: learn: 0.5057000 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remaining: 839ms 104: learn: 0.0644726 total: 11.1s remaining: 739ms 105: learn: 0.0637749 total: 11.2s remaining: 636ms 106: learn: 0.0632878 total: 11.3s remaining: 530ms 107: learn: 0.0626710 total: 11.5s remaining: 426ms 108: learn: 0.0621451 total: 11.7s remaining: 321ms 109: learn: 0.0617842 total: 11.8s remaining: 215ms 110: learn: 0.0614374 total: 11.9s remaining: 107ms 111: learn: 0.0608474 total: 12s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 87.79 - Recall_Train: 100.00 - AUPRC_Train: 99.61 - Accuracy_Train: 93.04 - F1-Score_Train: 93.50 - Precision_Test: 1.17 - Recall_Test: 97.62 - AUPRC_Test: 60.47 - Accuracy_Test: 86.13 - F1-Score_Test: 2.31 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 112 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 12.44 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6605847 total: 74.9ms remaining: 8.31s 1: learn: 0.6215119 total: 155ms remaining: 8.5s 2: learn: 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[I 2024-12-19 14:16:02,525] Trial 7 finished with value: 64.92665531792693 and parameters: {'learning_rate': 0.012295431777560331, 'max_depth': 5, 'n_estimators': 112, 'scale_pos_weight': 12.440833233561072}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 90.72 - Recall_Train: 99.89 - AUPRC_Train: 99.65 - Accuracy_Train: 94.84 - F1-Score_Train: 95.09 - Precision_Test: 1.47 - Recall_Test: 91.27 - AUPRC_Test: 66.30 - Accuracy_Test: 89.72 - F1-Score_Test: 2.90 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 112 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 12.44 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 64.9267 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6328904 total: 78.9ms remaining: 11.6s 1: learn: 0.5820174 total: 154ms remaining: 11.2s 2: learn: 0.5369840 total: 230ms remaining: 11.1s 3: learn: 0.4958011 total: 313ms remaining: 11.3s 4: learn: 0.4493009 total: 394ms remaining: 11.3s 5: learn: 0.4053742 total: 479ms remaining: 11.3s 6: learn: 0.3738167 total: 575ms remaining: 11.6s 7: learn: 0.3455484 total: 667ms remaining: 11.7s 8: learn: 0.3165438 total: 747ms 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0.0221782 total: 14.5s remaining: 199ms 146: learn: 0.0220132 total: 14.6s remaining: 99.6ms 147: learn: 0.0219104 total: 14.8s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 96.35 - Recall_Train: 100.00 - AUPRC_Train: 99.93 - Accuracy_Train: 98.11 - F1-Score_Train: 98.14 - Precision_Test: 3.79 - Recall_Test: 91.27 - AUPRC_Test: 71.31 - Accuracy_Test: 96.08 - F1-Score_Test: 7.27 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 148 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.01 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6390756 total: 74.2ms remaining: 10.9s 1: learn: 0.5802155 total: 155ms remaining: 11.3s 2: learn: 0.5237646 total: 255ms remaining: 12.3s 3: learn: 0.4825340 total: 339ms remaining: 12.2s 4: learn: 0.4446192 total: 418ms remaining: 12s 5: learn: 0.4146794 total: 496ms remaining: 11.7s 6: learn: 0.3847542 total: 583ms remaining: 11.7s 7: learn: 0.3596930 total: 661ms remaining: 11.6s 8: 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remaining: 306ms 145: learn: 0.0349542 total: 14.9s remaining: 204ms 146: learn: 0.0348249 total: 14.9s remaining: 102ms 147: learn: 0.0346445 total: 15s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 94.91 - Recall_Train: 100.00 - AUPRC_Train: 99.86 - Accuracy_Train: 97.32 - F1-Score_Train: 97.39 - Precision_Test: 2.93 - Recall_Test: 95.24 - AUPRC_Test: 63.39 - Accuracy_Test: 94.68 - F1-Score_Test: 5.68 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 148 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.01 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6362143 total: 74.8ms remaining: 11s 1: learn: 0.5783390 total: 156ms remaining: 11.4s 2: learn: 0.5421574 total: 229ms remaining: 11s 3: learn: 0.4964515 total: 316ms remaining: 11.4s 4: learn: 0.4545840 total: 393ms remaining: 11.3s 5: learn: 0.4134586 total: 475ms remaining: 11.2s 6: learn: 0.3884590 total: 558ms remaining: 11.2s 7: learn: 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[I 2024-12-19 14:16:54,410] Trial 8 finished with value: 67.96113894002374 and parameters: {'learning_rate': 0.02247052435322354, 'max_depth': 5, 'n_estimators': 148, 'scale_pos_weight': 8.009318253271207}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 95.50 - Recall_Train: 100.00 - AUPRC_Train: 99.86 - Accuracy_Train: 97.65 - F1-Score_Train: 97.70 - Precision_Test: 3.10 - Recall_Test: 90.48 - AUPRC_Test: 69.19 - Accuracy_Test: 95.23 - F1-Score_Test: 6.00 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 148 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.01 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 67.9611 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6653267 total: 76.6ms remaining: 17.8s 1: learn: 0.6343843 total: 157ms remaining: 18.2s 2: learn: 0.6029590 total: 241ms remaining: 18.5s 3: learn: 0.5795372 total: 328ms remaining: 18.8s 4: learn: 0.5534308 total: 421ms remaining: 19.3s 5: learn: 0.5306989 total: 514ms remaining: 19.5s 6: learn: 0.5044069 total: 605ms remaining: 19.6s 7: learn: 0.4812742 total: 688ms remaining: 19.4s 8: learn: 0.4574218 total: 770ms 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devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 234 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: 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95.8ms 233: learn: 0.0469942 total: 22.4s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 94.69 - Recall_Train: 100.00 - AUPRC_Train: 99.82 - Accuracy_Train: 97.19 - F1-Score_Train: 97.27 - Precision_Test: 2.83 - Recall_Test: 96.03 - AUPRC_Test: 60.64 - Accuracy_Test: 94.44 - F1-Score_Test: 5.49 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 234 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6657628 total: 154ms remaining: 35.8s 1: learn: 0.6351756 total: 310ms remaining: 36s 2: learn: 0.6075162 total: 472ms remaining: 36.3s 3: learn: 0.5846874 total: 625ms remaining: 35.9s 4: learn: 0.5616980 total: 785ms remaining: 36s 5: learn: 0.5449695 total: 922ms remaining: 35s 6: learn: 0.5243615 total: 1.07s remaining: 34.6s 7: learn: 0.5021051 total: 1.21s remaining: 34.1s 8: learn: 0.4804896 total: 1.36s remaining: 33.9s 9: learn: 0.4668107 total: 1.51s 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[I 2024-12-19 14:18:12,511] Trial 9 finished with value: 65.83384449024851 and parameters: {'learning_rate': 0.011217151265462048, 'max_depth': 5, 'n_estimators': 234, 'scale_pos_weight': 6.4905218679657}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 95.29 - Recall_Train: 99.82 - AUPRC_Train: 99.82 - Accuracy_Train: 97.44 - F1-Score_Train: 97.50 - Precision_Test: 2.93 - Recall_Test: 90.48 - AUPRC_Test: 67.52 - Accuracy_Test: 94.94 - F1-Score_Test: 5.68 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 234 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.01 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 65.8338 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4931447 total: 64.6ms remaining: 11s 1: learn: 0.3492495 total: 132ms remaining: 11.1s 2: learn: 0.2603746 total: 198ms remaining: 11.1s 3: learn: 0.1927722 total: 267ms remaining: 11.1s 4: learn: 0.1703140 total: 338ms remaining: 11.2s 5: learn: 0.1424015 total: 401ms remaining: 11s 6: learn: 0.1317398 total: 492ms remaining: 11.5s 7: learn: 0.1180704 total: 562ms remaining: 11.5s 8: learn: 0.1069296 total: 633ms 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eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 171 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.85 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4875881 total: 64ms remaining: 10.9s 1: learn: 0.3627264 total: 127ms remaining: 10.8s 2: learn: 0.2793745 total: 190ms remaining: 10.6s 3: learn: 0.2288357 total: 258ms remaining: 10.8s 4: learn: 0.1981843 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12.9s remaining: 804ms 161: learn: 0.0154435 total: 13s remaining: 723ms 162: learn: 0.0153412 total: 13.1s remaining: 641ms 163: learn: 0.0152288 total: 13.1s remaining: 561ms 164: learn: 0.0151638 total: 13.2s remaining: 480ms 165: learn: 0.0150606 total: 13.3s remaining: 400ms 166: learn: 0.0149658 total: 13.3s remaining: 319ms 167: learn: 0.0148767 total: 13.4s remaining: 239ms 168: learn: 0.0148017 total: 13.5s remaining: 159ms 169: learn: 0.0147279 total: 13.6s remaining: 79.9ms 170: learn: 0.0146430 total: 13.7s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 96.75 - Recall_Train: 100.00 - AUPRC_Train: 99.94 - Accuracy_Train: 98.32 - F1-Score_Train: 98.35 - Precision_Test: 4.41 - Recall_Test: 92.86 - AUPRC_Test: 70.98 - Accuracy_Test: 96.61 - F1-Score_Test: 8.43 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 171 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.85 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando 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[I 2024-12-19 14:19:00,347] Trial 10 finished with value: 69.21627930577772 and parameters: {'learning_rate': 0.09459749516165813, 'max_depth': 3, 'n_estimators': 171, 'scale_pos_weight': 10.84733886433144}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.29 - Recall_Train: 100.00 - AUPRC_Train: 99.94 - Accuracy_Train: 98.61 - F1-Score_Train: 98.62 - Precision_Test: 5.10 - Recall_Test: 90.48 - AUPRC_Test: 71.77 - Accuracy_Test: 97.15 - F1-Score_Test: 9.66 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 171 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.85 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 69.2163 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5473609 total: 91.4ms remaining: 16.2s 1: learn: 0.4021043 total: 185ms remaining: 16.3s 2: learn: 0.3212695 total: 280ms remaining: 16.3s 3: learn: 0.2633772 total: 369ms remaining: 16.1s 4: learn: 0.2032855 total: 463ms remaining: 16s 5: learn: 0.1563412 total: 559ms remaining: 16s 6: learn: 0.1300931 total: 680ms remaining: 16.6s 7: learn: 0.1113860 total: 769ms remaining: 16.3s 8: learn: 0.0956070 total: 864ms 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F1-Score_Test: 22.54 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 178 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 14.93 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: 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0.0047374 total: 18.4s remaining: 423ms 174: learn: 0.0046821 total: 18.5s remaining: 317ms 175: learn: 0.0046281 total: 18.6s remaining: 211ms 176: learn: 0.0045527 total: 18.7s remaining: 106ms 177: learn: 0.0045318 total: 18.8s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.77 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.38 - F1-Score_Train: 99.38 - Precision_Test: 10.41 - Recall_Test: 89.68 - AUPRC_Test: 68.85 - Accuracy_Test: 98.69 - F1-Score_Test: 18.66 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 178 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 14.93 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5459035 total: 82.1ms remaining: 14.5s 1: learn: 0.4004351 total: 177ms remaining: 15.6s 2: learn: 0.3128886 total: 273ms remaining: 15.9s 3: learn: 0.2517210 total: 374ms remaining: 16.3s 4: learn: 0.2013117 total: 471ms remaining: 16.3s 5: learn: 0.1618378 total: 563ms 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[I 2024-12-19 14:20:10,939] Trial 11 finished with value: 73.84096687604249 and parameters: {'learning_rate': 0.0564778558554291, 'max_depth': 6, 'n_estimators': 178, 'scale_pos_weight': 14.93460217793416}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.91 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.45 - F1-Score_Train: 99.45 - Precision_Test: 11.51 - Recall_Test: 88.89 - AUPRC_Test: 74.27 - Accuracy_Test: 98.83 - F1-Score_Test: 20.38 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 178 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 14.93 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 73.8410 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5307292 total: 92.2ms remaining: 14.7s 1: learn: 0.3750641 total: 187ms remaining: 14.9s 2: learn: 0.2922774 total: 280ms remaining: 14.7s 3: learn: 0.2208457 total: 397ms remaining: 15.6s 4: learn: 0.1796055 total: 495ms remaining: 15.5s 5: learn: 0.1540549 total: 576ms remaining: 14.9s 6: learn: 0.1234457 total: 678ms remaining: 14.9s 7: learn: 0.1081048 total: 763ms remaining: 14.6s 8: learn: 0.0926606 total: 858ms 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99.56 - F1-Score_Train: 99.56 - Precision_Test: 13.02 - Recall_Test: 86.51 - AUPRC_Test: 75.99 - Accuracy_Test: 99.01 - F1-Score_Test: 22.64 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 161 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 14.99 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5231649 total: 85.4ms remaining: 13.7s 1: learn: 0.3992002 total: 176ms remaining: 14s 2: learn: 0.3138507 total: 266ms remaining: 14s 3: learn: 0.2461577 total: 370ms remaining: 14.5s 4: learn: 0.2025193 total: 472ms remaining: 14.7s 5: learn: 0.1752763 total: 576ms remaining: 14.9s 6: learn: 0.1552711 total: 680ms remaining: 15s 7: learn: 0.1369309 total: 764ms remaining: 14.6s 8: learn: 0.1217553 total: 858ms remaining: 14.5s 9: learn: 0.1067203 total: 941ms remaining: 14.2s 10: learn: 0.0921446 total: 1.03s remaining: 14.1s 11: learn: 0.0862587 total: 1.13s remaining: 14s 12: learn: 0.0806701 total: 1.22s remaining: 13.9s 13: learn: 0.0750996 total: 1.31s remaining: 13.8s 14: learn: 0.0681347 total: 1.41s remaining: 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16.5s remaining: 975ms 152: learn: 0.0045455 total: 16.5s remaining: 865ms 153: learn: 0.0045182 total: 16.6s remaining: 756ms 154: learn: 0.0044874 total: 16.7s remaining: 647ms 155: learn: 0.0044576 total: 16.8s remaining: 539ms 156: learn: 0.0044123 total: 16.9s remaining: 431ms 157: learn: 0.0043227 total: 17s remaining: 323ms 158: learn: 0.0042654 total: 17.1s remaining: 215ms 159: learn: 0.0042237 total: 17.2s remaining: 108ms 160: learn: 0.0041972 total: 17.3s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.85 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.42 - F1-Score_Train: 99.42 - Precision_Test: 10.89 - Recall_Test: 89.68 - AUPRC_Test: 71.62 - Accuracy_Test: 98.75 - F1-Score_Test: 19.42 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 161 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 14.99 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5290729 total: 80.1ms remaining: 12.8s 1: 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[I 2024-12-19 14:21:11,754] Trial 12 finished with value: 72.37429312947954 and parameters: {'learning_rate': 0.06363954218996214, 'max_depth': 6, 'n_estimators': 161, 'scale_pos_weight': 14.99247144578231}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.86 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.43 - F1-Score_Train: 99.43 - Precision_Test: 10.67 - Recall_Test: 88.10 - AUPRC_Test: 69.51 - Accuracy_Test: 98.74 - F1-Score_Test: 19.04 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 161 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 14.99 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 72.3743 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5311492 total: 92.8ms remaining: 9.56s 1: learn: 0.3999432 total: 193ms remaining: 9.84s 2: learn: 0.3064717 total: 282ms remaining: 9.5s 3: learn: 0.2240020 total: 388ms remaining: 9.69s 4: learn: 0.1742127 total: 483ms remaining: 9.57s 5: learn: 0.1464662 total: 563ms remaining: 9.19s 6: learn: 0.1162967 total: 668ms remaining: 9.26s 7: learn: 0.1034400 total: 765ms remaining: 9.18s 8: learn: 0.0880208 total: 857ms 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0.0066456 total: 8.43s remaining: 1.53s 88: learn: 0.0065574 total: 8.53s remaining: 1.44s 89: learn: 0.0064052 total: 8.63s remaining: 1.34s 90: learn: 0.0063608 total: 8.72s remaining: 1.25s 91: learn: 0.0063367 total: 8.8s remaining: 1.15s 92: learn: 0.0063074 total: 8.88s remaining: 1.05s 93: learn: 0.0062430 total: 8.96s remaining: 954ms 94: learn: 0.0061129 total: 9.07s remaining: 859ms 95: learn: 0.0060388 total: 9.19s remaining: 766ms 96: learn: 0.0059647 total: 9.35s remaining: 674ms 97: learn: 0.0058901 total: 9.49s remaining: 581ms 98: learn: 0.0057920 total: 9.66s remaining: 488ms 99: learn: 0.0056629 total: 9.83s remaining: 393ms 100: learn: 0.0055603 total: 10s remaining: 298ms 101: learn: 0.0054961 total: 10.2s remaining: 200ms 102: learn: 0.0053979 total: 10.4s remaining: 101ms 103: learn: 0.0053418 total: 10.6s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.67 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.33 - F1-Score_Train: 99.33 - Precision_Test: 9.54 - Recall_Test: 88.10 - AUPRC_Test: 76.39 - Accuracy_Test: 98.58 - F1-Score_Test: 17.22 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 104 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.72 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5237014 total: 171ms remaining: 17.6s 1: learn: 0.3887235 total: 346ms remaining: 17.7s 2: learn: 0.3061664 total: 523ms remaining: 17.6s 3: learn: 0.2516754 total: 615ms remaining: 15.4s 4: learn: 0.2010913 total: 702ms remaining: 13.9s 5: learn: 0.1748055 total: 804ms remaining: 13.1s 6: learn: 0.1549169 total: 881ms remaining: 12.2s 7: learn: 0.1424425 total: 985ms remaining: 11.8s 8: learn: 0.1252698 total: 1.07s remaining: 11.4s 9: learn: 0.1059923 total: 1.17s remaining: 11s 10: learn: 0.0956049 total: 1.25s remaining: 10.6s 11: learn: 0.0858901 total: 1.36s remaining: 10.4s 12: learn: 0.0779036 total: 1.46s remaining: 10.2s 13: 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remaining: 1.16s 92: learn: 0.0092521 total: 8.98s remaining: 1.06s 93: learn: 0.0091436 total: 9.06s remaining: 964ms 94: learn: 0.0090440 total: 9.16s remaining: 868ms 95: learn: 0.0089812 total: 9.24s remaining: 770ms 96: learn: 0.0088142 total: 9.33s remaining: 674ms 97: learn: 0.0087411 total: 9.42s remaining: 577ms 98: learn: 0.0086298 total: 9.52s remaining: 481ms 99: learn: 0.0084901 total: 9.6s remaining: 384ms 100: learn: 0.0084161 total: 9.69s remaining: 288ms 101: learn: 0.0082527 total: 9.79s remaining: 192ms 102: learn: 0.0080768 total: 9.88s remaining: 96ms 103: learn: 0.0080339 total: 9.96s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.19 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.08 - F1-Score_Train: 99.09 - Precision_Test: 7.62 - Recall_Test: 90.48 - AUPRC_Test: 72.18 - Accuracy_Test: 98.14 - F1-Score_Test: 14.05 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 104 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.72 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 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[I 2024-12-19 14:21:51,819] Trial 13 finished with value: 73.57994734889388 and parameters: {'learning_rate': 0.06611682054595477, 'max_depth': 6, 'n_estimators': 104, 'scale_pos_weight': 11.721329337016055}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.37 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 99.17 - F1-Score_Train: 99.18 - Precision_Test: 8.18 - Recall_Test: 88.89 - AUPRC_Test: 72.18 - Accuracy_Test: 98.30 - F1-Score_Test: 14.97 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 104 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.72 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 73.5799 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5740955 total: 154ms remaining: 33.1s 1: learn: 0.4734523 total: 328ms remaining: 35.1s 2: learn: 0.3974636 total: 510ms remaining: 36.2s 3: learn: 0.3214541 total: 699ms remaining: 37s 4: learn: 0.2627932 total: 893ms remaining: 37.7s 5: learn: 0.2137600 total: 1.08s remaining: 37.9s 6: learn: 0.1799575 total: 1.26s remaining: 37.8s 7: learn: 0.1536607 total: 1.43s remaining: 37.2s 8: learn: 0.1367628 total: 1.61s 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None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 216 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.37 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 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one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 216 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.37 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5797772 total: 87.1ms remaining: 18.7s 1: learn: 0.4854029 total: 177ms remaining: 19s 2: learn: 0.3945383 total: 271ms remaining: 19.3s 3: learn: 0.3227531 total: 372ms remaining: 19.7s 4: learn: 0.2762917 total: 463ms remaining: 19.5s 5: learn: 0.2396950 total: 545ms remaining: 19.1s 6: learn: 0.2092467 total: 638ms remaining: 19s 7: learn: 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[I 2024-12-19 14:23:13,602] Trial 14 finished with value: 75.70747631678337 and parameters: {'learning_rate': 0.04518205664082046, 'max_depth': 6, 'n_estimators': 216, 'scale_pos_weight': 8.366830949370627}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.28 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.64 - F1-Score_Train: 99.64 - Precision_Test: 15.98 - Recall_Test: 86.51 - AUPRC_Test: 76.09 - Accuracy_Test: 99.21 - F1-Score_Test: 26.98 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 216 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.37 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 75.7075 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5991828 total: 63.3ms remaining: 15.6s 1: learn: 0.5220682 total: 128ms remaining: 15.7s 2: learn: 0.4554222 total: 194ms remaining: 15.8s 3: learn: 0.3944889 total: 259ms remaining: 15.8s 4: learn: 0.3505515 total: 342ms remaining: 16.6s 5: learn: 0.3081532 total: 411ms remaining: 16.6s 6: learn: 0.2669720 total: 491ms remaining: 16.9s 7: learn: 0.2418820 total: 569ms remaining: 17.1s 8: learn: 0.2205648 total: 631ms 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allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 248 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.68 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6047898 total: 63.2ms remaining: 15.6s 1: learn: 0.5290494 total: 126ms remaining: 15.5s 2: learn: 0.4650087 total: 189ms remaining: 15.4s 3: learn: 0.4131651 total: 254ms remaining: 15.5s 4: learn: 0.3764208 total: 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allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 248 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.68 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6017198 total: 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[I 2024-12-19 14:24:22,636] Trial 15 finished with value: 67.80101170304454 and parameters: {'learning_rate': 0.03857647736265097, 'max_depth': 3, 'n_estimators': 248, 'scale_pos_weight': 8.684304389345261}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 95.95 - Recall_Train: 99.99 - AUPRC_Train: 99.87 - Accuracy_Train: 97.88 - F1-Score_Train: 97.93 - Precision_Test: 3.38 - Recall_Test: 90.48 - AUPRC_Test: 67.36 - Accuracy_Test: 95.64 - F1-Score_Test: 6.53 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 248 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.68 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 67.8010 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6196067 total: 87.5ms remaining: 18.2s 1: learn: 0.5500102 total: 182ms remaining: 18.8s 2: learn: 0.4825292 total: 276ms remaining: 19s 3: learn: 0.4260889 total: 379ms remaining: 19.4s 4: learn: 0.3766515 total: 479ms remaining: 19.5s 5: learn: 0.3368406 total: 574ms remaining: 19.4s 6: learn: 0.3033531 total: 679ms remaining: 19.6s 7: learn: 0.2714120 total: 790ms remaining: 19.9s 8: learn: 0.2510016 total: 887ms 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allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 209 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.97 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6238656 total: 171ms remaining: 35.6s 1: learn: 0.5634402 total: 345ms remaining: 35.7s 2: learn: 0.5060996 total: 460ms remaining: 31.6s 3: learn: 0.4544470 total: 554ms remaining: 28.4s 4: learn: 0.4062524 total: 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dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 209 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.97 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6228610 total: 86.3ms remaining: 18s 1: 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[I 2024-12-19 14:25:41,643] Trial 16 finished with value: 72.7388417743842 and parameters: {'learning_rate': 0.026658237113297848, 'max_depth': 6, 'n_estimators': 209, 'scale_pos_weight': 8.96907757599555}. Best is trial 4 with value: 76.71888513333191.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.09 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 99.03 - F1-Score_Train: 99.04 - Precision_Test: 7.14 - Recall_Test: 88.89 - AUPRC_Test: 72.16 - Accuracy_Test: 98.04 - F1-Score_Test: 13.22 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 209 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 8.97 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 72.7388 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5145279 total: 93.1ms remaining: 23.5s 1: learn: 0.3759899 total: 185ms remaining: 23.2s 2: learn: 0.2829656 total: 266ms remaining: 22.2s 3: learn: 0.2256432 total: 360ms remaining: 22.4s 4: learn: 0.1832321 total: 439ms remaining: 21.8s 5: learn: 0.1568421 total: 520ms remaining: 21.4s 6: learn: 0.1276152 total: 620ms remaining: 21.8s 7: learn: 0.1085092 total: 702ms remaining: 21.5s 8: learn: 0.0988769 total: 777ms 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1.09s 242: learn: 0.0032037 total: 24.2s remaining: 996ms 243: learn: 0.0031881 total: 24.4s remaining: 898ms 244: learn: 0.0031584 total: 24.5s remaining: 800ms 245: learn: 0.0031584 total: 24.6s remaining: 700ms 246: learn: 0.0031584 total: 24.7s remaining: 600ms 247: learn: 0.0031584 total: 24.8s remaining: 500ms 248: learn: 0.0031584 total: 24.9s remaining: 401ms 249: learn: 0.0031584 total: 25s remaining: 301ms 250: learn: 0.0031584 total: 25.1s remaining: 200ms 251: learn: 0.0031584 total: 25.3s remaining: 100ms 252: learn: 0.0031584 total: 25.4s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.59 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.79 - F1-Score_Train: 99.79 - Precision_Test: 21.48 - Recall_Test: 87.30 - AUPRC_Test: 79.51 - Accuracy_Test: 99.44 - F1-Score_Test: 34.48 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 253 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.54 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando 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total: 23.3s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.45 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.72 - F1-Score_Train: 99.73 - Precision_Test: 19.61 - Recall_Test: 88.10 - AUPRC_Test: 74.19 - Accuracy_Test: 99.37 - F1-Score_Test: 32.08 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 253 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.54 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5228661 total: 151ms remaining: 38s 1: learn: 0.3859115 total: 300ms remaining: 37.6s 2: learn: 0.2964520 total: 460ms remaining: 38.4s 3: learn: 0.2442947 total: 621ms remaining: 38.6s 4: learn: 0.2017852 total: 803ms remaining: 39.8s 5: learn: 0.1650164 total: 953ms remaining: 39.2s 6: learn: 0.1456196 total: 1.12s remaining: 39.5s 7: learn: 0.1260202 total: 1.27s remaining: 39.1s 8: learn: 0.1152291 total: 1.42s remaining: 38.6s 9: learn: 0.1028731 total: 1.57s remaining: 38.3s 10: learn: 0.0966118 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[I 2024-12-19 14:27:02,254] Trial 17 finished with value: 76.82995176096074 and parameters: {'learning_rate': 0.07428627404365123, 'max_depth': 5, 'n_estimators': 253, 'scale_pos_weight': 7.539212242386238}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.52 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.76 - F1-Score_Train: 99.76 - Precision_Test: 20.77 - Recall_Test: 85.71 - AUPRC_Test: 76.79 - Accuracy_Test: 99.43 - F1-Score_Test: 33.44 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 253 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.54 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 76.8300 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5263613 total: 80.8ms remaining: 21.3s 1: learn: 0.4121834 total: 155ms remaining: 20.3s 2: learn: 0.3237400 total: 236ms remaining: 20.6s 3: learn: 0.2510401 total: 335ms remaining: 21.7s 4: learn: 0.2008746 total: 417ms remaining: 21.6s 5: learn: 0.1655795 total: 504ms remaining: 21.7s 6: learn: 0.1440500 total: 597ms remaining: 21.9s 7: learn: 0.1179604 total: 680ms remaining: 21.8s 8: learn: 0.1085345 total: 754ms 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26.3s remaining: 201ms 262: learn: 0.0036449 total: 26.4s remaining: 100ms 263: learn: 0.0036449 total: 26.5s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.52 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.76 - F1-Score_Train: 99.76 - Precision_Test: 19.60 - Recall_Test: 85.71 - AUPRC_Test: 75.48 - Accuracy_Test: 99.38 - F1-Score_Test: 31.91 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 264 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.47 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5423557 total: 74.8ms remaining: 19.7s 1: learn: 0.4344608 total: 148ms remaining: 19.3s 2: learn: 0.3422554 total: 230ms remaining: 20s 3: learn: 0.2753539 total: 324ms remaining: 21.1s 4: learn: 0.2306095 total: 403ms remaining: 20.9s 5: learn: 0.1933691 total: 504ms remaining: 21.7s 6: learn: 0.1733145 total: 596ms remaining: 21.9s 7: learn: 0.1543653 total: 678ms remaining: 21.7s 8: learn: 0.1448106 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305ms 261: learn: 0.0045399 total: 26.7s remaining: 203ms 262: learn: 0.0045399 total: 26.7s remaining: 102ms 263: learn: 0.0045398 total: 26.8s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.42 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.71 - F1-Score_Train: 99.71 - Precision_Test: 19.61 - Recall_Test: 88.10 - AUPRC_Test: 75.09 - Accuracy_Test: 99.37 - F1-Score_Test: 32.08 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 264 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.47 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5350895 total: 89.7ms remaining: 23.6s 1: learn: 0.4036828 total: 167ms remaining: 21.9s 2: learn: 0.3265101 total: 242ms remaining: 21s 3: learn: 0.2687210 total: 328ms remaining: 21.3s 4: learn: 0.2146894 total: 404ms remaining: 20.9s 5: learn: 0.1871147 total: 474ms remaining: 20.4s 6: learn: 0.1615733 total: 567ms remaining: 20.8s 7: 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[I 2024-12-19 14:28:30,358] Trial 18 finished with value: 75.76072978196702 and parameters: {'learning_rate': 0.06850266651634628, 'max_depth': 5, 'n_estimators': 264, 'scale_pos_weight': 7.470440912112707}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.54 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.77 - F1-Score_Train: 99.77 - Precision_Test: 21.05 - Recall_Test: 85.71 - AUPRC_Test: 76.72 - Accuracy_Test: 99.44 - F1-Score_Test: 33.80 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 264 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.47 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 75.7607 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5020543 total: 84.5ms remaining: 25.2s 1: learn: 0.3591791 total: 158ms remaining: 23.5s 2: learn: 0.2568284 total: 234ms remaining: 23.1s 3: learn: 0.2186300 total: 324ms remaining: 23.9s 4: learn: 0.1627414 total: 407ms remaining: 23.9s 5: learn: 0.1286575 total: 507ms remaining: 24.7s 6: learn: 0.1116524 total: 594ms remaining: 24.8s 7: learn: 0.0902882 total: 682ms remaining: 24.8s 8: learn: 0.0810592 total: 759ms 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SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.34 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.67 - F1-Score_Train: 99.67 - Precision_Test: 16.25 - Recall_Test: 87.30 - AUPRC_Test: 77.75 - Accuracy_Test: 99.22 - F1-Score_Test: 27.40 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 299 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.26 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4924829 total: 78.9ms remaining: 23.5s 1: learn: 0.3594689 total: 157ms remaining: 23.2s 2: learn: 0.2927070 total: 233ms remaining: 23s 3: learn: 0.2238305 total: 331ms remaining: 24.4s 4: learn: 0.1891208 total: 402ms remaining: 23.7s 5: learn: 0.1578421 total: 477ms remaining: 23.3s 6: learn: 0.1361190 total: 564ms remaining: 23.5s 7: learn: 0.1173186 total: 650ms remaining: 23.6s 8: learn: 0.1030176 total: 729ms remaining: 23.5s 9: learn: 0.0965088 total: 822ms remaining: 23.8s 10: learn: 0.0886856 total: 901ms remaining: 23.6s 11: learn: 0.0818489 total: 985ms 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F1-Score_Train: 99.70 - Precision_Test: 17.75 - Recall_Test: 88.89 - AUPRC_Test: 74.52 - Accuracy_Test: 99.29 - F1-Score_Test: 29.59 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 299 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.26 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: 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[I 2024-12-19 14:30:05,104] Trial 19 finished with value: 74.99710469798667 and parameters: {'learning_rate': 0.08233568205551695, 'max_depth': 5, 'n_estimators': 299, 'scale_pos_weight': 11.25520316622741}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.18 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.59 - F1-Score_Train: 99.59 - Precision_Test: 14.59 - Recall_Test: 85.71 - AUPRC_Test: 72.72 - Accuracy_Test: 99.13 - F1-Score_Test: 24.94 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 299 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 11.26 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 74.9971 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5230593 total: 72.7ms remaining: 18.1s 1: learn: 0.3998375 total: 140ms remaining: 17.3s 2: learn: 0.3197351 total: 213ms remaining: 17.6s 3: learn: 0.2510058 total: 326ms remaining: 20s 4: learn: 0.2065171 total: 399ms remaining: 19.6s 5: learn: 0.1706473 total: 478ms remaining: 19.4s 6: learn: 0.1553893 total: 561ms remaining: 19.5s 7: learn: 0.1328442 total: 635ms remaining: 19.2s 8: learn: 0.1192944 total: 710ms 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max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 250 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.04 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5327678 total: 109ms remaining: 27s 1: learn: 0.4150200 total: 225ms remaining: 27.9s 2: learn: 0.3279974 total: 362ms 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99.29 - F1-Score_Test: 29.82 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 250 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.04 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: 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[I 2024-12-19 14:31:20,266] Trial 20 finished with value: 75.11058411780488 and parameters: {'learning_rate': 0.08045994957415391, 'max_depth': 4, 'n_estimators': 250, 'scale_pos_weight': 5.035013283171664}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.38 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.69 - F1-Score_Train: 99.69 - Precision_Test: 17.13 - Recall_Test: 87.30 - AUPRC_Test: 71.44 - Accuracy_Test: 99.27 - F1-Score_Test: 28.65 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 250 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.04 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 75.1106 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5159139 total: 153ms remaining: 40.6s 1: learn: 0.3977486 total: 301ms remaining: 39.7s 2: learn: 0.3092914 total: 480ms remaining: 42.1s 3: learn: 0.2349535 total: 643ms remaining: 42.1s 4: learn: 0.1861807 total: 800ms remaining: 41.8s 5: learn: 0.1475838 total: 905ms remaining: 39.2s 6: learn: 0.1281250 total: 975ms remaining: 36.1s 7: learn: 0.1094297 total: 1.07s remaining: 34.7s 8: learn: 0.1008056 total: 1.15s 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classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 266 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.41 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5321953 total: 151ms remaining: 40s 1: learn: 0.3972426 total: 300ms remaining: 39.6s 2: learn: 0.3295590 total: 460ms remaining: 40.3s 3: learn: 0.2674780 total: 610ms remaining: 40s 4: learn: 0.2281870 total: 756ms remaining: 39.5s 5: learn: 0.1942593 total: 909ms remaining: 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metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 266 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.41 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5252505 total: 75.2ms remaining: 19.9s 1: learn: 0.3893171 total: 154ms remaining: 20.4s 2: learn: 0.3056325 total: 228ms remaining: 19.9s 3: 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[I 2024-12-19 14:32:46,726] Trial 21 finished with value: 76.56551175173995 and parameters: {'learning_rate': 0.07337676836051153, 'max_depth': 5, 'n_estimators': 266, 'scale_pos_weight': 7.414132310718996}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.51 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.75 - F1-Score_Train: 99.75 - Precision_Test: 20.45 - Recall_Test: 86.51 - AUPRC_Test: 75.97 - Accuracy_Test: 99.41 - F1-Score_Test: 33.08 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 266 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.41 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 76.5655 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4996998 total: 78.3ms remaining: 21.5s 1: learn: 0.3547361 total: 160ms remaining: 21.9s 2: learn: 0.2689399 total: 239ms remaining: 21.7s 3: learn: 0.2081967 total: 346ms remaining: 23.4s 4: learn: 0.1548221 total: 430ms remaining: 23.2s 5: learn: 0.1270316 total: 513ms remaining: 23s 6: learn: 0.1041767 total: 609ms remaining: 23.3s 7: learn: 0.0888289 total: 691ms remaining: 23s 8: learn: 0.0810009 total: 782ms 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Accuracy_Test: 99.25 - F1-Score_Test: 28.21 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 275 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.59 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 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allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 275 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.59 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5083078 total: 75.2ms remaining: 20.6s 1: learn: 0.3629067 total: 153ms remaining: 20.9s 2: learn: 0.2737336 total: 230ms remaining: 20.8s 3: learn: 0.2221708 total: 324ms remaining: 22s 4: learn: 0.1835073 total: 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[I 2024-12-19 14:34:17,964] Trial 22 finished with value: 74.79473450830527 and parameters: {'learning_rate': 0.07738097901544849, 'max_depth': 5, 'n_estimators': 275, 'scale_pos_weight': 9.58893520201917}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.36 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.68 - F1-Score_Train: 99.68 - Precision_Test: 17.08 - Recall_Test: 86.51 - AUPRC_Test: 74.94 - Accuracy_Test: 99.27 - F1-Score_Test: 28.53 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 275 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.59 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 74.7947 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5753285 total: 76.9ms remaining: 18.5s 1: learn: 0.4653052 total: 154ms remaining: 18.4s 2: learn: 0.3834945 total: 235ms remaining: 18.7s 3: learn: 0.3175096 total: 331ms remaining: 19.7s 4: learn: 0.2652700 total: 419ms remaining: 19.9s 5: learn: 0.2288249 total: 504ms remaining: 19.8s 6: learn: 0.1927140 total: 608ms remaining: 20.4s 7: learn: 0.1681305 total: 691ms remaining: 20.2s 8: learn: 0.1459056 total: 780ms 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(Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.38 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.69 - F1-Score_Train: 99.69 - Precision_Test: 16.19 - Recall_Test: 85.71 - AUPRC_Test: 75.88 - Accuracy_Test: 99.23 - F1-Score_Test: 27.24 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 242 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.99 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5800480 total: 148ms remaining: 35.6s 1: learn: 0.4826641 total: 302ms remaining: 36.3s 2: learn: 0.3991852 total: 446ms remaining: 35.6s 3: learn: 0.3404135 total: 602ms remaining: 35.8s 4: learn: 0.2991011 total: 778ms remaining: 36.9s 5: learn: 0.2623428 total: 934ms remaining: 36.7s 6: learn: 0.2271460 total: 1.11s remaining: 37.3s 7: learn: 0.2033475 total: 1.27s remaining: 37.1s 8: learn: 0.1840732 total: 1.43s remaining: 37s 9: learn: 0.1726910 total: 1.58s remaining: 36.7s 10: learn: 0.1575042 total: 1.74s remaining: 36.5s 11: learn: 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99.15 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.57 - F1-Score_Train: 99.57 - Precision_Test: 14.41 - Recall_Test: 88.89 - AUPRC_Test: 70.79 - Accuracy_Test: 99.09 - F1-Score_Test: 24.81 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 242 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.99 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5762286 total: 94ms remaining: 22.6s 1: learn: 0.4737941 total: 169ms remaining: 20.3s 2: learn: 0.4043772 total: 251ms remaining: 20s 3: learn: 0.3366448 total: 341ms remaining: 20.3s 4: learn: 0.2911392 total: 421ms remaining: 20s 5: learn: 0.2528720 total: 502ms remaining: 19.7s 6: learn: 0.2219249 total: 607ms remaining: 20.4s 7: learn: 0.1971720 total: 682ms remaining: 20s 8: learn: 0.1794378 total: 761ms remaining: 19.7s 9: learn: 0.1647089 total: 857ms remaining: 19.9s 10: learn: 0.1528591 total: 930ms remaining: 19.5s 11: learn: 0.1401986 total: 1s remaining: 19.2s 12: learn: 0.1295716 total: 1.1s remaining: 19.4s 13: learn: 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[I 2024-12-19 14:35:38,534] Trial 23 finished with value: 72.8818479109883 and parameters: {'learning_rate': 0.049655270980061436, 'max_depth': 5, 'n_estimators': 242, 'scale_pos_weight': 6.987733767823069}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.15 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.57 - F1-Score_Train: 99.57 - Precision_Test: 13.96 - Recall_Test: 86.51 - AUPRC_Test: 71.97 - Accuracy_Test: 99.08 - F1-Score_Test: 24.04 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 242 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.99 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 72.8818 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5074617 total: 69.9ms remaining: 13.2s 1: learn: 0.3757317 total: 137ms remaining: 12.8s 2: learn: 0.2922161 total: 207ms remaining: 12.9s 3: learn: 0.2386786 total: 277ms remaining: 12.9s 4: learn: 0.1896158 total: 363ms remaining: 13.4s 5: learn: 0.1578107 total: 451ms remaining: 13.8s 6: learn: 0.1291689 total: 538ms remaining: 14.1s 7: learn: 0.1186391 total: 605ms remaining: 13.8s 8: learn: 0.1067697 total: 673ms 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remaining: 539ms 184: learn: 0.0062931 total: 16.6s remaining: 449ms 185: learn: 0.0062564 total: 16.7s remaining: 359ms 186: learn: 0.0062225 total: 16.7s remaining: 269ms 187: learn: 0.0061847 total: 16.8s remaining: 179ms 188: learn: 0.0061620 total: 16.9s remaining: 89.4ms 189: learn: 0.0061169 total: 17s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.27 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.63 - F1-Score_Train: 99.63 - Precision_Test: 14.32 - Recall_Test: 87.30 - AUPRC_Test: 74.66 - Accuracy_Test: 99.10 - F1-Score_Test: 24.61 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 190 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.31 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5191676 total: 74.3ms remaining: 14s 1: learn: 0.3922853 total: 168ms remaining: 15.7s 2: learn: 0.3085411 total: 244ms remaining: 15.2s 3: learn: 0.2479370 total: 329ms remaining: 15.3s 4: learn: 0.2101545 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180: learn: 0.0102809 total: 16.3s remaining: 809ms 181: learn: 0.0101566 total: 16.3s remaining: 718ms 182: learn: 0.0101399 total: 16.4s remaining: 628ms 183: learn: 0.0100574 total: 16.5s remaining: 537ms 184: learn: 0.0099363 total: 16.5s remaining: 447ms 185: learn: 0.0098496 total: 16.6s remaining: 357ms 186: learn: 0.0097605 total: 16.7s remaining: 268ms 187: learn: 0.0097391 total: 16.9s remaining: 179ms 188: learn: 0.0096242 total: 17s remaining: 90ms 189: learn: 0.0095364 total: 17.2s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.94 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.46 - F1-Score_Train: 99.47 - Precision_Test: 12.15 - Recall_Test: 89.68 - AUPRC_Test: 75.28 - Accuracy_Test: 98.89 - F1-Score_Test: 21.40 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 190 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.31 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5167763 total: 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[I 2024-12-19 14:36:38,173] Trial 24 finished with value: 73.65938167950104 and parameters: {'learning_rate': 0.08600313201336994, 'max_depth': 4, 'n_estimators': 190, 'scale_pos_weight': 6.312659747077875}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.99 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 99.49 - F1-Score_Train: 99.49 - Precision_Test: 12.69 - Recall_Test: 87.30 - AUPRC_Test: 71.04 - Accuracy_Test: 98.97 - F1-Score_Test: 22.16 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 190 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.31 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 73.6594 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5397280 total: 78.5ms remaining: 20.6s 1: learn: 0.4317848 total: 154ms remaining: 20.2s 2: learn: 0.3449528 total: 239ms remaining: 20.8s 3: learn: 0.2743022 total: 351ms remaining: 22.8s 4: learn: 0.2244071 total: 429ms remaining: 22.2s 5: learn: 0.1810411 total: 509ms remaining: 21.9s 6: learn: 0.1542563 total: 617ms remaining: 22.7s 7: learn: 0.1306620 total: 698ms remaining: 22.3s 8: learn: 0.1137963 total: 781ms 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261: learn: 0.0035475 total: 25.2s remaining: 193ms 262: learn: 0.0035475 total: 25.3s remaining: 96.4ms 263: learn: 0.0035475 total: 25.5s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.53 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.76 - F1-Score_Train: 99.76 - Precision_Test: 19.22 - Recall_Test: 86.51 - AUPRC_Test: 75.97 - Accuracy_Test: 99.37 - F1-Score_Test: 31.46 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 264 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.80 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5548287 total: 74.8ms remaining: 19.7s 1: learn: 0.4526924 total: 149ms remaining: 19.6s 2: learn: 0.3628503 total: 229ms remaining: 19.9s 3: learn: 0.2996388 total: 325ms remaining: 21.1s 4: learn: 0.2497080 total: 405ms remaining: 21s 5: learn: 0.2125170 total: 486ms remaining: 20.9s 6: learn: 0.1868980 total: 586ms remaining: 21.5s 7: 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0.0050259 total: 24.1s remaining: 277ms 261: learn: 0.0049938 total: 24.2s remaining: 184ms 262: learn: 0.0049492 total: 24.2s remaining: 92.2ms 263: learn: 0.0049491 total: 24.3s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.37 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.68 - F1-Score_Train: 99.68 - Precision_Test: 18.27 - Recall_Test: 88.89 - AUPRC_Test: 73.53 - Accuracy_Test: 99.31 - F1-Score_Test: 30.31 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 264 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.80 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5478250 total: 154ms remaining: 40.5s 1: learn: 0.4213744 total: 302ms remaining: 39.6s 2: learn: 0.3466952 total: 458ms remaining: 39.8s 3: learn: 0.2882748 total: 608ms remaining: 39.5s 4: learn: 0.2327166 total: 780ms remaining: 40.4s 5: learn: 0.2028769 total: 930ms remaining: 40s 6: learn: 0.1792657 total: 1.08s 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[I 2024-12-19 14:38:03,839] Trial 25 finished with value: 74.93209955678623 and parameters: {'learning_rate': 0.06170667436486901, 'max_depth': 5, 'n_estimators': 264, 'scale_pos_weight': 7.802165848882661}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.44 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.72 - F1-Score_Train: 99.72 - Precision_Test: 18.89 - Recall_Test: 86.51 - AUPRC_Test: 75.30 - Accuracy_Test: 99.35 - F1-Score_Test: 31.01 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 264 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.80 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 74.9321 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5866410 total: 189ms remaining: 43.5s 1: learn: 0.4952628 total: 431ms remaining: 49.3s 2: learn: 0.4090996 total: 649ms remaining: 49.4s 3: learn: 0.3403545 total: 843ms remaining: 47.8s 4: learn: 0.2845607 total: 1.05s remaining: 47.3s 5: learn: 0.2378555 total: 1.24s remaining: 46.5s 6: learn: 0.2035385 total: 1.44s remaining: 46.1s 7: learn: 0.1890474 total: 1.62s remaining: 45.1s 8: learn: 0.1646969 total: 1.85s 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ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 231 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5916181 total: 91.9ms remaining: 21.1s 1: learn: 0.5128143 total: 184ms remaining: 21s 2: 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- Recall_Test: 89.68 - AUPRC_Test: 71.65 - Accuracy_Test: 98.89 - F1-Score_Test: 21.40 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 231 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 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[I 2024-12-19 14:39:33,117] Trial 26 finished with value: 74.38254646492007 and parameters: {'learning_rate': 0.038345268912684985, 'max_depth': 6, 'n_estimators': 231, 'scale_pos_weight': 10.485025796706932}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.99 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.49 - F1-Score_Train: 99.49 - Precision_Test: 12.03 - Recall_Test: 88.10 - AUPRC_Test: 74.14 - Accuracy_Test: 98.90 - F1-Score_Test: 21.16 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 231 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 10.49 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 74.3825 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5137427 total: 81.7ms remaining: 10.2s 1: learn: 0.3869694 total: 161ms remaining: 9.96s 2: learn: 0.2891022 total: 239ms remaining: 9.82s 3: learn: 0.2011878 total: 358ms remaining: 10.9s 4: learn: 0.1769189 total: 432ms remaining: 10.4s 5: learn: 0.1392676 total: 512ms remaining: 10.2s 6: learn: 0.1160256 total: 610ms remaining: 10.4s 7: learn: 0.0971263 total: 692ms remaining: 10.2s 8: learn: 0.0886974 total: 771ms 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CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.58 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 99.28 - F1-Score_Train: 99.28 - Precision_Test: 8.81 - Recall_Test: 89.68 - AUPRC_Test: 71.45 - Accuracy_Test: 98.42 - F1-Score_Test: 16.04 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 126 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 12.21 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5056830 total: 77ms remaining: 9.62s 1: learn: 0.3721478 total: 157ms remaining: 9.76s 2: learn: 0.2822670 total: 238ms remaining: 9.77s 3: learn: 0.2370196 total: 335ms remaining: 10.2s 4: learn: 0.1958003 total: 417ms remaining: 10.1s 5: learn: 0.1619697 total: 488ms remaining: 9.76s 6: learn: 0.1387515 total: 581ms remaining: 9.87s 7: learn: 0.1256454 total: 655ms remaining: 9.66s 8: learn: 0.1137274 total: 728ms remaining: 9.47s 9: learn: 0.1036607 total: 845ms remaining: 9.81s 10: learn: 0.0941198 total: 926ms remaining: 9.68s 11: learn: 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Recall_Train: 100.00 - AUPRC_Train: 99.95 - Accuracy_Train: 99.03 - F1-Score_Train: 99.04 - Precision_Test: 7.14 - Recall_Test: 92.06 - AUPRC_Test: 65.74 - Accuracy_Test: 97.97 - F1-Score_Test: 13.26 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 126 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 12.21 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5124124 total: 90ms remaining: 11.3s 1: learn: 0.3610189 total: 171ms remaining: 10.6s 2: learn: 0.2574768 total: 255ms remaining: 10.5s 3: learn: 0.1983481 total: 347ms remaining: 10.6s 4: learn: 0.1674476 total: 418ms remaining: 10.1s 5: learn: 0.1380557 total: 496ms remaining: 9.92s 6: learn: 0.1193563 total: 592ms remaining: 10.1s 7: learn: 0.1082942 total: 672ms remaining: 9.92s 8: learn: 0.0966132 total: 750ms remaining: 9.75s 9: learn: 0.0876250 total: 845ms remaining: 9.81s 10: learn: 0.0782519 total: 924ms remaining: 9.66s 11: learn: 0.0736635 total: 1.01s remaining: 9.56s 12: learn: 0.0683811 total: 1.1s remaining: 9.6s 13: 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[I 2024-12-19 14:40:19,248] Trial 27 finished with value: 69.34335533891107 and parameters: {'learning_rate': 0.07542042576277558, 'max_depth': 5, 'n_estimators': 126, 'scale_pos_weight': 12.209948677934136}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.19 - Recall_Train: 100.00 - AUPRC_Train: 99.96 - Accuracy_Train: 99.08 - F1-Score_Train: 99.09 - Precision_Test: 7.39 - Recall_Test: 88.89 - AUPRC_Test: 70.84 - Accuracy_Test: 98.11 - F1-Score_Test: 13.64 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 126 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.08 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 12.21 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 69.3434 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6524236 total: 69.5ms remaining: 13.7s 1: learn: 0.6094063 total: 147ms remaining: 14.4s 2: learn: 0.5721941 total: 221ms remaining: 14.4s 3: learn: 0.5388106 total: 327ms remaining: 15.9s 4: learn: 0.5050950 total: 400ms remaining: 15.5s 5: learn: 0.4766414 total: 474ms remaining: 15.2s 6: learn: 0.4506095 total: 569ms remaining: 15.5s 7: learn: 0.4276737 total: 644ms remaining: 15.3s 8: learn: 0.4056967 total: 716ms 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AUPRC_Test: 66.27 - Accuracy_Test: 93.64 - F1-Score_Test: 4.61 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 198 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.19 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 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18.4s remaining: 576ms 192: learn: 0.0462207 total: 18.5s remaining: 480ms 193: learn: 0.0460421 total: 18.7s remaining: 385ms 194: learn: 0.0458706 total: 18.8s remaining: 290ms 195: learn: 0.0457639 total: 19s remaining: 194ms 196: learn: 0.0455483 total: 19.1s remaining: 97ms 197: learn: 0.0453078 total: 19.3s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 92.23 - Recall_Train: 100.00 - AUPRC_Train: 99.75 - Accuracy_Train: 95.79 - F1-Score_Train: 95.96 - Precision_Test: 1.92 - Recall_Test: 97.62 - AUPRC_Test: 64.85 - Accuracy_Test: 91.61 - F1-Score_Test: 3.77 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 198 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.19 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6536731 total: 89.8ms remaining: 17.7s 1: learn: 0.6228669 total: 161ms remaining: 15.8s 2: learn: 0.5900271 total: 227ms remaining: 14.8s 3: learn: 0.5569117 total: 301ms remaining: 14.6s 4: learn: 0.5234371 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[I 2024-12-19 14:41:21,753] Trial 28 finished with value: 65.1714938148073 and parameters: {'learning_rate': 0.016026776755532404, 'max_depth': 4, 'n_estimators': 198, 'scale_pos_weight': 9.19373718334758}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 93.63 - Recall_Train: 99.92 - AUPRC_Train: 99.74 - Accuracy_Train: 96.56 - F1-Score_Train: 96.67 - Precision_Test: 2.17 - Recall_Test: 91.27 - AUPRC_Test: 64.40 - Accuracy_Test: 93.07 - F1-Score_Test: 4.24 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 4 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 198 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.02 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 9.19 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 65.1715 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4935286 total: 66.3ms remaining: 10.1s 1: learn: 0.3695169 total: 134ms remaining: 10.1s 2: learn: 0.2759904 total: 202ms remaining: 10.1s 3: learn: 0.2183985 total: 270ms remaining: 10s 4: learn: 0.1833070 total: 349ms remaining: 10.3s 5: learn: 0.1639299 total: 413ms remaining: 10.1s 6: learn: 0.1470237 total: 477ms remaining: 9.95s 7: learn: 0.1383021 total: 570ms remaining: 10.3s 8: learn: 0.1276789 total: 643ms 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12.4s remaining: 596ms 146: learn: 0.0152826 total: 12.5s remaining: 511ms 147: learn: 0.0151632 total: 12.6s remaining: 425ms 148: learn: 0.0149426 total: 12.6s remaining: 339ms 149: learn: 0.0148275 total: 12.7s remaining: 254ms 150: learn: 0.0147606 total: 12.8s remaining: 170ms 151: learn: 0.0146910 total: 12.8s remaining: 84.5ms 152: learn: 0.0145392 total: 12.9s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.17 - Recall_Train: 100.00 - AUPRC_Train: 99.93 - Accuracy_Train: 99.07 - F1-Score_Train: 99.08 - Precision_Test: 7.09 - Recall_Test: 89.68 - AUPRC_Test: 66.93 - Accuracy_Test: 98.01 - F1-Score_Test: 13.15 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 153 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.70 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5084963 total: 65.7ms remaining: 9.98s 1: learn: 0.3692600 total: 131ms remaining: 9.87s 2: learn: 0.2918772 total: 193ms remaining: 9.63s 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remaining: 1.09s 140: learn: 0.0243342 total: 11.8s remaining: 1s 141: learn: 0.0242007 total: 11.8s remaining: 918ms 142: learn: 0.0239937 total: 11.9s remaining: 833ms 143: learn: 0.0237905 total: 12s remaining: 748ms 144: learn: 0.0235471 total: 12.1s remaining: 665ms 145: learn: 0.0233593 total: 12.1s remaining: 581ms 146: learn: 0.0231395 total: 12.2s remaining: 497ms 147: learn: 0.0230286 total: 12.2s remaining: 413ms 148: learn: 0.0228325 total: 12.3s remaining: 330ms 149: learn: 0.0226860 total: 12.4s remaining: 247ms 150: learn: 0.0225670 total: 12.4s remaining: 165ms 151: learn: 0.0223764 total: 12.5s remaining: 82.1ms 152: learn: 0.0222708 total: 12.6s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.46 - Recall_Train: 99.98 - AUPRC_Train: 99.95 - Accuracy_Train: 98.69 - F1-Score_Train: 98.70 - Precision_Test: 5.54 - Recall_Test: 92.06 - AUPRC_Test: 74.19 - Accuracy_Test: 97.35 - F1-Score_Test: 10.46 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 153 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.70 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: 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[I 2024-12-19 14:42:06,211] Trial 29 finished with value: 71.18715885589056 and parameters: {'learning_rate': 0.09688742604917952, 'max_depth': 3, 'n_estimators': 153, 'scale_pos_weight': 5.703085534477173}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 97.66 - Recall_Train: 100.00 - AUPRC_Train: 99.94 - Accuracy_Train: 98.80 - F1-Score_Train: 98.82 - Precision_Test: 5.71 - Recall_Test: 88.89 - AUPRC_Test: 72.44 - Accuracy_Test: 97.51 - F1-Score_Test: 10.73 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 3 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 153 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.70 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 71.1872 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6192777 total: 86ms remaining: 24.4s 1: learn: 0.5298423 total: 181ms remaining: 25.6s 2: learn: 0.4567616 total: 274ms remaining: 25.7s 3: learn: 0.3991627 total: 384ms remaining: 27s 4: learn: 0.3561209 total: 473ms remaining: 26.5s 5: learn: 0.3162578 total: 565ms remaining: 26.3s 6: learn: 0.2813961 total: 678ms remaining: 26.9s 7: learn: 0.2521694 total: 769ms remaining: 26.6s 8: learn: 0.2240104 total: 862ms 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None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 285 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.75 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6198503 total: 92.9ms remaining: 26.4s 1: learn: 0.5431025 total: 187ms remaining: 26.4s 2: learn: 0.4884452 total: 277ms remaining: 26.1s 3: learn: 0.4342536 total: 399ms remaining: 28s 4: learn: 0.3909642 total: 492ms remaining: 27.5s 5: learn: 0.3523016 total: 593ms remaining: 27.6s 6: learn: 0.3226028 total: 706ms 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allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 285 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.75 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6202890 total: 169ms remaining: 47.9s 1: learn: 0.5443297 total: 352ms remaining: 49.8s 2: learn: 0.4732453 total: 535ms remaining: 50.3s 3: learn: 0.4226335 total: 697ms remaining: 48.9s 4: learn: 0.3775496 total: 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[I 2024-12-19 14:43:55,220] Trial 30 finished with value: 75.80834607680522 and parameters: {'learning_rate': 0.030836379878527897, 'max_depth': 6, 'n_estimators': 285, 'scale_pos_weight': 5.7536982118548305}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.33 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.66 - F1-Score_Train: 99.66 - Precision_Test: 16.98 - Recall_Test: 87.30 - AUPRC_Test: 76.71 - Accuracy_Test: 99.26 - F1-Score_Test: 28.42 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 285 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.75 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 75.8083 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6214611 total: 88.2ms remaining: 24.8s 1: learn: 0.5343131 total: 184ms remaining: 25.8s 2: learn: 0.4627989 total: 275ms remaining: 25.6s 3: learn: 0.4059747 total: 391ms remaining: 27.1s 4: learn: 0.3573342 total: 486ms remaining: 26.9s 5: learn: 0.3181761 total: 578ms remaining: 26.6s 6: learn: 0.2842328 total: 692ms remaining: 27.2s 7: learn: 0.2538960 total: 782ms remaining: 26.8s 8: learn: 0.2257316 total: 888ms 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remaining: 115ms 281: learn: 0.0053061 total: 32.3s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.48 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.74 - F1-Score_Train: 99.74 - Precision_Test: 18.66 - Recall_Test: 86.51 - AUPRC_Test: 75.52 - Accuracy_Test: 99.34 - F1-Score_Test: 30.70 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 282 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.74 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6220895 total: 97.6ms remaining: 27.4s 1: learn: 0.5474713 total: 192ms remaining: 26.8s 2: learn: 0.4938332 total: 282ms remaining: 26.2s 3: learn: 0.4405410 total: 394ms remaining: 27.4s 4: learn: 0.3977845 total: 485ms remaining: 26.8s 5: learn: 0.3592920 total: 572ms remaining: 26.3s 6: learn: 0.3297195 total: 680ms remaining: 26.7s 7: learn: 0.2986728 total: 787ms remaining: 26.9s 8: learn: 0.2752179 total: 870ms remaining: 26.4s 9: learn: 0.2512153 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32.2s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.24 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.62 - F1-Score_Train: 99.62 - Precision_Test: 16.05 - Recall_Test: 88.89 - AUPRC_Test: 75.15 - Accuracy_Test: 99.20 - F1-Score_Test: 27.18 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 282 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.74 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6225581 total: 88.2ms remaining: 24.8s 1: learn: 0.5486515 total: 177ms remaining: 24.7s 2: learn: 0.4789358 total: 265ms remaining: 24.7s 3: learn: 0.4332096 total: 376ms remaining: 26.1s 4: learn: 0.4000851 total: 456ms remaining: 25.3s 5: learn: 0.3606694 total: 539ms remaining: 24.8s 6: learn: 0.3179819 total: 682ms remaining: 26.8s 7: learn: 0.2953867 total: 761ms remaining: 26.1s 8: learn: 0.2632512 total: 848ms remaining: 25.7s 9: learn: 0.2421221 total: 954ms remaining: 26s 10: learn: 0.2241515 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[I 2024-12-19 14:45:39,283] Trial 31 finished with value: 75.63142654627858 and parameters: {'learning_rate': 0.029871745940680643, 'max_depth': 6, 'n_estimators': 282, 'scale_pos_weight': 5.742025260880375}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.29 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.64 - F1-Score_Train: 99.65 - Precision_Test: 16.11 - Recall_Test: 88.10 - AUPRC_Test: 76.22 - Accuracy_Test: 99.21 - F1-Score_Test: 27.24 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 282 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.74 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 75.6314 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6257779 total: 181ms remaining: 47.7s 1: learn: 0.5505313 total: 342ms remaining: 45s 2: learn: 0.4876470 total: 534ms remaining: 46.6s 3: learn: 0.4378464 total: 716ms remaining: 46.7s 4: learn: 0.3905146 total: 879ms remaining: 45.7s 5: learn: 0.3491902 total: 1.05s remaining: 45.5s 6: learn: 0.3120769 total: 1.25s remaining: 45.9s 7: learn: 0.2809215 total: 1.4s remaining: 45.1s 8: learn: 0.2543328 total: 1.59s 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one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 265 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.21 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6309154 total: 163ms remaining: 42.9s 1: learn: 0.5567945 total: 333ms remaining: 43.8s 2: learn: 0.4968656 total: 536ms remaining: 46.8s 3: learn: 0.4487708 total: 711ms remaining: 46.4s 4: learn: 0.4058856 total: 896ms remaining: 46.6s 5: learn: 0.3765183 total: 1.05s remaining: 45.5s 6: learn: 0.3436615 total: 1.25s remaining: 46.2s 7: 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None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 265 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.21 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.6288953 total: 86.6ms remaining: 22.9s 1: learn: 0.5690307 total: 192ms remaining: 25.3s 2: learn: 0.5074231 total: 285ms remaining: 24.9s 3: learn: 0.4637357 total: 389ms remaining: 25.4s 4: learn: 0.4151384 total: 477ms remaining: 24.8s 5: learn: 0.3745992 total: 562ms remaining: 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[I 2024-12-19 14:47:20,616] Trial 32 finished with value: 72.7404439438197 and parameters: {'learning_rate': 0.025573344355910715, 'max_depth': 6, 'n_estimators': 265, 'scale_pos_weight': 7.214464450742685}. Best is trial 17 with value: 76.82995176096074.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 98.83 - Recall_Train: 100.00 - AUPRC_Train: 99.97 - Accuracy_Train: 99.41 - F1-Score_Train: 99.41 - Precision_Test: 11.29 - Recall_Test: 88.10 - AUPRC_Test: 73.64 - Accuracy_Test: 98.82 - F1-Score_Test: 20.02 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 265 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.03 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 7.21 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 72.7404 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4619808 total: 90.8ms remaining: 26.3s 1: learn: 0.3221746 total: 185ms remaining: 26.7s 2: learn: 0.2222340 total: 275ms remaining: 26.4s 3: learn: 0.1647534 total: 389ms remaining: 27.9s 4: learn: 0.1286910 total: 480ms remaining: 27.5s 5: learn: 0.1103094 total: 571ms remaining: 27.1s 6: learn: 0.0895204 total: 697ms remaining: 28.3s 7: learn: 0.0790815 total: 786ms remaining: 27.8s 8: learn: 0.0706291 total: 874ms 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per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 291 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.28 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando 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Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.78 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.89 - F1-Score_Train: 99.89 - Precision_Test: 35.37 - Recall_Test: 87.30 - AUPRC_Test: 79.86 - Accuracy_Test: 99.71 - F1-Score_Test: 50.34 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 291 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.28 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4353343 total: 85.5ms remaining: 24.8s 1: learn: 0.3184158 total: 173ms remaining: 25s 2: learn: 0.2415977 total: 253ms remaining: 24.3s 3: learn: 0.1844016 total: 359ms remaining: 25.7s 4: learn: 0.1466091 total: 452ms remaining: 25.9s 5: learn: 0.1247542 total: 555ms remaining: 26.4s 6: learn: 0.1096538 total: 671ms remaining: 27.2s 7: learn: 0.1006168 total: 752ms remaining: 26.6s 8: learn: 0.0922725 total: 836ms remaining: 26.2s 9: learn: 0.0843200 total: 949ms remaining: 26.7s 10: learn: 0.0764265 total: 1.05s remaining: 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[I 2024-12-19 14:49:00,319] Trial 33 finished with value: 80.40946207404374 and parameters: {'learning_rate': 0.09923534902000127, 'max_depth': 6, 'n_estimators': 291, 'scale_pos_weight': 5.27629579592143}. Best is trial 33 with value: 80.40946207404374.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.75 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.87 - F1-Score_Train: 99.87 - Precision_Test: 31.05 - Recall_Test: 86.51 - AUPRC_Test: 80.29 - Accuracy_Test: 99.65 - F1-Score_Test: 45.70 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 291 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.28 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 80.4095 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4877405 total: 76.5ms remaining: 22.6s 1: learn: 0.3389861 total: 153ms remaining: 22.5s 2: learn: 0.2422950 total: 233ms remaining: 22.7s 3: learn: 0.1880405 total: 342ms remaining: 24.9s 4: learn: 0.1485937 total: 426ms remaining: 24.8s 5: learn: 0.1254717 total: 503ms remaining: 24.3s 6: learn: 0.1075883 total: 618ms remaining: 25.5s 7: learn: 0.0964560 total: 698ms remaining: 25.1s 8: learn: 0.0889306 total: 771ms 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F1-Score_Train: 99.85 - Precision_Test: 25.60 - Recall_Test: 84.92 - AUPRC_Test: 79.03 - Accuracy_Test: 99.56 - F1-Score_Test: 39.34 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 296 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.12 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4948363 total: 80.8ms remaining: 23.8s 1: learn: 0.3627427 total: 160ms remaining: 23.6s 2: learn: 0.2711008 total: 241ms remaining: 23.5s 3: learn: 0.2206589 total: 358ms remaining: 26.1s 4: learn: 0.1890859 total: 436ms remaining: 25.4s 5: learn: 0.1624794 total: 525ms remaining: 25.4s 6: learn: 0.1434857 total: 626ms remaining: 25.8s 7: learn: 0.1286784 total: 705ms remaining: 25.4s 8: learn: 0.1188180 total: 779ms remaining: 24.8s 9: learn: 0.1059125 total: 885ms remaining: 25.3s 10: learn: 0.0998139 total: 960ms remaining: 24.9s 11: learn: 0.0945433 total: 1.04s remaining: 24.6s 12: learn: 0.0897870 total: 1.14s remaining: 24.7s 13: learn: 0.0850561 total: 1.21s remaining: 24.4s 14: learn: 0.0802890 total: 1.29s remaining: 24.2s 15: 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dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 296 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.12 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4832185 total: 77.6ms remaining: 22.9s 1: 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[I 2024-12-19 14:50:32,300] Trial 34 finished with value: 77.09665448747673 and parameters: {'learning_rate': 0.09821891331885638, 'max_depth': 5, 'n_estimators': 296, 'scale_pos_weight': 5.121024209926356}. Best is trial 33 with value: 80.40946207404374.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.62 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.81 - F1-Score_Train: 99.81 - Precision_Test: 25.29 - Recall_Test: 86.51 - AUPRC_Test: 77.26 - Accuracy_Test: 99.55 - F1-Score_Test: 39.14 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 296 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.12 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 77.0967 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4633701 total: 91.8ms remaining: 27.2s 1: learn: 0.3241201 total: 181ms remaining: 26.7s 2: learn: 0.2291723 total: 268ms remaining: 26.3s 3: learn: 0.1701728 total: 383ms remaining: 28.1s 4: learn: 0.1270233 total: 481ms remaining: 28.1s 5: learn: 0.1086097 total: 569ms remaining: 27.6s 6: learn: 0.0916520 total: 680ms remaining: 28.2s 7: learn: 0.0814642 total: 769ms remaining: 27.8s 8: learn: 0.0717767 total: 858ms 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100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.87 - F1-Score_Train: 99.87 - Precision_Test: 28.73 - Recall_Test: 84.13 - AUPRC_Test: 80.75 - Accuracy_Test: 99.62 - F1-Score_Test: 42.83 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 297 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.16 - logging_level: None - 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per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 297 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.16 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 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[I 2024-12-19 14:52:11,563] Trial 35 finished with value: 79.16809783426771 and parameters: {'learning_rate': 0.098965298814483, 'max_depth': 6, 'n_estimators': 297, 'scale_pos_weight': 5.1636674707054615}. Best is trial 33 with value: 80.40946207404374.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.76 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.88 - F1-Score_Train: 99.88 - Precision_Test: 31.49 - Recall_Test: 85.71 - AUPRC_Test: 78.40 - Accuracy_Test: 99.66 - F1-Score_Test: 46.06 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 297 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.16 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 79.1681 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4874039 total: 75.1ms remaining: 22.3s 1: learn: 0.3384427 total: 151ms remaining: 22.4s 2: learn: 0.2429694 total: 237ms remaining: 23.3s 3: learn: 0.1910255 total: 359ms remaining: 26.4s 4: learn: 0.1501800 total: 447ms remaining: 26.2s 5: learn: 0.1300545 total: 529ms remaining: 25.8s 6: learn: 0.1156125 total: 627ms remaining: 26.1s 7: learn: 0.1004736 total: 706ms remaining: 25.6s 8: learn: 0.0899072 total: 773ms 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con SMOTE - Sobreajuste: 1 - Precision_Train: 99.72 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.86 - F1-Score_Train: 99.86 - Precision_Test: 26.85 - Recall_Test: 83.33 - AUPRC_Test: 78.45 - Accuracy_Test: 99.59 - F1-Score_Test: 40.62 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 298 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.19 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4910591 total: 84.3ms remaining: 25s 1: learn: 0.3617510 total: 160ms remaining: 23.7s 2: learn: 0.2733040 total: 239ms remaining: 23.5s 3: learn: 0.2104339 total: 388ms remaining: 28.5s 4: learn: 0.1765844 total: 510ms remaining: 29.9s 5: learn: 0.1472855 total: 688ms remaining: 33.5s 6: learn: 0.1329715 total: 829ms remaining: 34.5s 7: learn: 0.1215824 total: 991ms remaining: 35.9s 8: learn: 0.1125742 total: 1.16s remaining: 37.1s 9: learn: 0.1042066 total: 1.32s remaining: 38.1s 10: learn: 0.0961468 total: 1.48s remaining: 38.7s 11: learn: 0.0910433 total: 1.66s remaining: 39.6s 12: learn: 0.0864008 total: 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99.62 - F1-Score_Test: 43.92 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 298 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.19 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: 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[I 2024-12-19 14:53:49,060] Trial 36 finished with value: 78.14508014095021 and parameters: {'learning_rate': 0.0982110654729356, 'max_depth': 5, 'n_estimators': 298, 'scale_pos_weight': 5.190090911358252}. Best is trial 33 with value: 80.40946207404374.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.73 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.86 - F1-Score_Train: 99.86 - Precision_Test: 29.67 - Recall_Test: 85.71 - AUPRC_Test: 78.19 - Accuracy_Test: 99.63 - F1-Score_Test: 44.08 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 5 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 298 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.19 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 78.1451 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4657781 total: 88ms remaining: 26s 1: learn: 0.2921732 total: 183ms remaining: 26.9s 2: learn: 0.2078858 total: 272ms remaining: 26.6s 3: learn: 0.1551787 total: 393ms remaining: 28.7s 4: learn: 0.1243450 total: 490ms remaining: 28.5s 5: learn: 0.0990054 total: 575ms remaining: 27.8s 6: learn: 0.0831818 total: 697ms remaining: 28.8s 7: learn: 0.0745376 total: 863ms remaining: 31.1s 8: learn: 0.0691002 total: 1.02s 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F1-Score_Train: 99.87 - Precision_Test: 30.51 - Recall_Test: 85.71 - AUPRC_Test: 80.41 - Accuracy_Test: 99.65 - F1-Score_Test: 45.00 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 296 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.02 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4825753 total: 185ms remaining: 54.5s 1: learn: 0.3384528 total: 350ms remaining: 51.5s 2: learn: 0.2506294 total: 521ms remaining: 50.9s 3: learn: 0.1984111 total: 690ms remaining: 50.4s 4: learn: 0.1611385 total: 842ms remaining: 49s 5: learn: 0.1382490 total: 1.02s remaining: 49.5s 6: learn: 0.1221695 total: 1.21s remaining: 49.8s 7: learn: 0.1127194 total: 1.38s remaining: 49.8s 8: learn: 0.1013016 total: 1.57s remaining: 49.9s 9: learn: 0.0926677 total: 1.75s remaining: 50.1s 10: learn: 0.0863344 total: 1.94s remaining: 50.2s 11: learn: 0.0817438 total: 2.12s remaining: 50.3s 12: learn: 0.0752214 total: 2.3s remaining: 50.1s 13: learn: 0.0718759 total: 2.47s remaining: 49.8s 14: learn: 0.0685945 total: 2.67s remaining: 50s 15: learn: 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ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 296 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.02 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4359159 total: 84.5ms remaining: 24.9s 1: learn: 0.3189518 total: 174ms remaining: 25.6s 2: 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[I 2024-12-19 14:55:31,911] Trial 37 finished with value: 80.67361029056296 and parameters: {'learning_rate': 0.0998524368637925, 'max_depth': 6, 'n_estimators': 296, 'scale_pos_weight': 5.024432396376165}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.78 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.89 - F1-Score_Train: 99.89 - Precision_Test: 34.38 - Recall_Test: 86.51 - AUPRC_Test: 81.21 - Accuracy_Test: 99.70 - F1-Score_Test: 49.21 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 296 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.02 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 80.6736 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4808432 total: 86.7ms remaining: 25.9s 1: learn: 0.3090605 total: 180ms remaining: 26.8s 2: learn: 0.2180256 total: 272ms remaining: 27s 3: learn: 0.1648453 total: 424ms remaining: 31.4s 4: learn: 0.1225896 total: 517ms remaining: 30.5s 5: learn: 0.0974431 total: 611ms remaining: 29.9s 6: learn: 0.0844820 total: 725ms remaining: 30.3s 7: learn: 0.0738980 total: 815ms remaining: 29.7s 8: learn: 0.0676328 total: 899ms 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remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.70 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.85 - F1-Score_Train: 99.85 - Precision_Test: 26.91 - Recall_Test: 86.51 - AUPRC_Test: 78.49 - Accuracy_Test: 99.58 - F1-Score_Test: 41.05 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 300 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.06 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4757795 total: 83.5ms remaining: 25s 1: learn: 0.3349661 total: 174ms remaining: 25.9s 2: learn: 0.2510108 total: 265ms remaining: 26.2s 3: learn: 0.1958661 total: 380ms remaining: 28.1s 4: learn: 0.1533913 total: 482ms remaining: 28.4s 5: learn: 0.1303593 total: 591ms remaining: 29s 6: learn: 0.1144222 total: 703ms remaining: 29.4s 7: learn: 0.1036227 total: 790ms remaining: 28.8s 8: learn: 0.0936241 total: 902ms remaining: 29.2s 9: learn: 0.0860994 total: 992ms remaining: 28.8s 10: learn: 0.0788624 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- Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.77 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.89 - F1-Score_Train: 99.89 - Precision_Test: 34.89 - Recall_Test: 88.89 - AUPRC_Test: 78.76 - Accuracy_Test: 99.70 - F1-Score_Test: 50.11 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 300 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.06 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4843108 total: 84.6ms remaining: 25.3s 1: learn: 0.3192416 total: 170ms remaining: 25.3s 2: learn: 0.2246759 total: 257ms remaining: 25.5s 3: learn: 0.1637588 total: 373ms remaining: 27.6s 4: learn: 0.1376057 total: 472ms remaining: 27.9s 5: learn: 0.1179659 total: 555ms remaining: 27.2s 6: learn: 0.1031389 total: 663ms remaining: 27.8s 7: learn: 0.0929596 total: 748ms remaining: 27.3s 8: learn: 0.0837348 total: 837ms remaining: 27.1s 9: learn: 0.0760804 total: 947ms remaining: 27.5s 10: learn: 0.0695936 total: 1.04s remaining: 27.3s 11: learn: 0.0647149 total: 1.14s remaining: 27.3s 12: 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[I 2024-12-19 14:57:16,174] Trial 38 finished with value: 78.87409870357558 and parameters: {'learning_rate': 0.09745835195680903, 'max_depth': 6, 'n_estimators': 300, 'scale_pos_weight': 6.057068837515901}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.71 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.86 - F1-Score_Train: 99.86 - Precision_Test: 29.03 - Recall_Test: 85.71 - AUPRC_Test: 79.37 - Accuracy_Test: 99.62 - F1-Score_Test: 43.37 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 300 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.10 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.06 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 78.8741 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4993088 total: 86.3ms remaining: 24.7s 1: learn: 0.3340092 total: 194ms remaining: 27.6s 2: learn: 0.2438067 total: 282ms remaining: 26.7s 3: learn: 0.1837864 total: 397ms remaining: 28.1s 4: learn: 0.1412311 total: 490ms remaining: 27.6s 5: learn: 0.1120741 total: 579ms remaining: 27.1s 6: learn: 0.0927377 total: 703ms remaining: 28.1s 7: learn: 0.0812817 total: 794ms remaining: 27.7s 8: learn: 0.0701734 total: 886ms 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allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 287 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.22 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4941965 total: 83.9ms remaining: 24s 1: learn: 0.3584212 total: 173ms remaining: 24.6s 2: learn: 0.2760034 total: 277ms remaining: 26.2s 3: learn: 0.2174277 total: 392ms remaining: 27.8s 4: learn: 0.1791629 total: 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- mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 287 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.22 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5022611 total: 82.4ms remaining: 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[I 2024-12-19 14:58:56,167] Trial 39 finished with value: 79.6702070023647 and parameters: {'learning_rate': 0.08714503523305407, 'max_depth': 6, 'n_estimators': 287, 'scale_pos_weight': 6.216876482022162}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.74 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.87 - F1-Score_Train: 99.87 - Precision_Test: 30.59 - Recall_Test: 85.71 - AUPRC_Test: 78.56 - Accuracy_Test: 99.65 - F1-Score_Test: 45.09 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 287 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.22 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 79.6702 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5530034 total: 186ms remaining: 53s 1: learn: 0.4264755 total: 370ms remaining: 52.5s 2: learn: 0.3374282 total: 583ms remaining: 55s 3: learn: 0.2669426 total: 746ms remaining: 52.6s 4: learn: 0.2104889 total: 942ms remaining: 53s 5: learn: 0.1738978 total: 1.1s remaining: 51.3s 6: learn: 0.1450735 total: 1.26s remaining: 50.1s 7: learn: 0.1229085 total: 1.44s remaining: 50s 8: learn: 0.1070658 total: 1.63s remaining: 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ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 286 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.58 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5615555 total: 170ms remaining: 48.6s 1: learn: 0.4293042 total: 351ms remaining: 49.9s 2: learn: 0.3467176 total: 556ms remaining: 52.4s 3: learn: 0.2870703 total: 724ms remaining: 51.1s 4: learn: 0.2458609 total: 918ms remaining: 51.6s 5: learn: 0.2090956 total: 1.09s remaining: 51s 6: learn: 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max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 286 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.58 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5589183 total: 85.5ms remaining: 24.4s 1: learn: 0.4304418 total: 171ms remaining: 24.2s 2: learn: 0.3387457 total: 257ms 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[I 2024-12-19 15:00:41,818] Trial 40 finished with value: 77.2542927985074 and parameters: {'learning_rate': 0.05779768842812921, 'max_depth': 6, 'n_estimators': 286, 'scale_pos_weight': 6.583256314651107}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.71 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.86 - F1-Score_Train: 99.86 - Precision_Test: 28.61 - Recall_Test: 86.51 - AUPRC_Test: 75.97 - Accuracy_Test: 99.61 - F1-Score_Test: 43.00 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 286 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.58 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 77.2543 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5002241 total: 86.4ms remaining: 23.6s 1: learn: 0.3354230 total: 180ms remaining: 24.5s 2: learn: 0.2436705 total: 270ms remaining: 24.4s 3: learn: 0.1842360 total: 386ms remaining: 26.1s 4: learn: 0.1414520 total: 477ms remaining: 25.6s 5: learn: 0.1094046 total: 574ms remaining: 25.6s 6: learn: 0.0905183 total: 689ms remaining: 26.3s 7: learn: 0.0792364 total: 774ms remaining: 25.7s 8: learn: 0.0704040 total: 869ms 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- per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 274 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.12 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 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min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 274 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.12 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5033207 total: 137ms remaining: 37.3s 1: learn: 0.3451872 total: 269ms remaining: 36.5s 2: learn: 0.2444421 total: 428ms remaining: 38.6s 3: learn: 0.1972065 total: 596ms remaining: 40.2s 4: learn: 0.1612259 total: 772ms remaining: 41.6s 5: learn: 0.1291969 total: 948ms remaining: 42.4s 6: learn: 0.1141496 total: 1.11s remaining: 42.3s 7: 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[I 2024-12-19 15:02:20,999] Trial 41 finished with value: 78.3979405034687 and parameters: {'learning_rate': 0.08686086644631785, 'max_depth': 6, 'n_estimators': 274, 'scale_pos_weight': 6.11935856633133}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.68 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.84 - F1-Score_Train: 99.84 - Precision_Test: 28.08 - Recall_Test: 84.92 - AUPRC_Test: 78.19 - Accuracy_Test: 99.61 - F1-Score_Test: 42.21 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 274 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.12 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 78.3979 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5001779 total: 87ms remaining: 25.1s 1: learn: 0.3353381 total: 183ms remaining: 26.2s 2: learn: 0.2436284 total: 273ms remaining: 26s 3: learn: 0.1841819 total: 389ms remaining: 27.7s 4: learn: 0.1413997 total: 498ms remaining: 28.3s 5: learn: 0.1107485 total: 584ms remaining: 27.6s 6: learn: 0.0912669 total: 697ms remaining: 28.1s 7: learn: 0.0797356 total: 790ms remaining: 27.7s 8: learn: 0.0699398 total: 889ms 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0.0027319 total: 31.1s remaining: 771ms 282: learn: 0.0027319 total: 31.2s remaining: 661ms 283: learn: 0.0027319 total: 31.2s remaining: 550ms 284: learn: 0.0027319 total: 31.3s remaining: 440ms 285: learn: 0.0027319 total: 31.4s remaining: 330ms 286: learn: 0.0027318 total: 31.5s remaining: 219ms 287: learn: 0.0027318 total: 31.6s remaining: 110ms 288: learn: 0.0027318 total: 31.7s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.72 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.86 - F1-Score_Train: 99.86 - Precision_Test: 28.23 - Recall_Test: 84.92 - AUPRC_Test: 80.09 - Accuracy_Test: 99.61 - F1-Score_Test: 42.38 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 289 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.10 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4953542 total: 96.5ms remaining: 27.8s 1: learn: 0.3580830 total: 192ms remaining: 27.5s 2: learn: 0.2677059 total: 281ms 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AUPRC_Test: 80.26 - Accuracy_Test: 99.66 - F1-Score_Test: 46.84 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 289 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.10 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 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[I 2024-12-19 15:04:03,668] Trial 42 finished with value: 80.1027112131879 and parameters: {'learning_rate': 0.0869330948309761, 'max_depth': 6, 'n_estimators': 289, 'scale_pos_weight': 6.103771581805754}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.71 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.85 - F1-Score_Train: 99.86 - Precision_Test: 29.92 - Recall_Test: 85.71 - AUPRC_Test: 79.95 - Accuracy_Test: 99.64 - F1-Score_Test: 44.35 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 289 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.10 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 80.1027 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5053669 total: 164ms remaining: 47.1s 1: learn: 0.3444938 total: 334ms remaining: 47.7s 2: learn: 0.2510589 total: 529ms remaining: 50.2s 3: learn: 0.1848036 total: 707ms remaining: 50.2s 4: learn: 0.1483785 total: 903ms remaining: 51.1s 5: learn: 0.1190417 total: 1.08s remaining: 50.7s 6: learn: 0.1014975 total: 1.25s remaining: 50.4s 7: learn: 0.0860082 total: 1.44s remaining: 50.3s 8: learn: 0.0765834 total: 1.61s 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None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 288 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.47 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5049969 total: 188ms remaining: 54.1s 1: learn: 0.3745148 total: 377ms remaining: 53.8s 2: learn: 0.2812619 total: 549ms remaining: 52.2s 3: 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None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 288 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.47 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 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[I 2024-12-19 15:05:43,154] Trial 43 finished with value: 79.65904428453553 and parameters: {'learning_rate': 0.08594556429165298, 'max_depth': 6, 'n_estimators': 288, 'scale_pos_weight': 5.469096902172981}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.74 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.87 - F1-Score_Train: 99.87 - Precision_Test: 30.36 - Recall_Test: 86.51 - AUPRC_Test: 80.17 - Accuracy_Test: 99.64 - F1-Score_Test: 44.95 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 288 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.47 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 79.6590 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5239965 total: 90.3ms remaining: 25.2s 1: learn: 0.3787417 total: 180ms remaining: 25s 2: learn: 0.2722155 total: 277ms remaining: 25.6s 3: learn: 0.2044810 total: 399ms remaining: 27.6s 4: learn: 0.1625860 total: 493ms remaining: 27.1s 5: learn: 0.1304929 total: 596ms remaining: 27.2s 6: learn: 0.1113307 total: 718ms remaining: 28s 7: learn: 0.0989650 total: 810ms remaining: 27.5s 8: learn: 0.0900454 total: 901ms 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Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.65 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.82 - F1-Score_Train: 99.83 - Precision_Test: 24.43 - Recall_Test: 84.92 - AUPRC_Test: 77.84 - Accuracy_Test: 99.53 - F1-Score_Test: 37.94 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 280 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.77 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5347037 total: 100ms remaining: 28s 1: learn: 0.3850156 total: 191ms remaining: 26.6s 2: learn: 0.3002381 total: 280ms remaining: 25.9s 3: learn: 0.2494719 total: 393ms remaining: 27.1s 4: learn: 0.2037251 total: 491ms remaining: 27s 5: learn: 0.1736350 total: 580ms remaining: 26.5s 6: learn: 0.1518708 total: 698ms remaining: 27.2s 7: learn: 0.1366560 total: 782ms remaining: 26.6s 8: learn: 0.1203033 total: 878ms remaining: 26.4s 9: learn: 0.1080805 total: 990ms remaining: 26.7s 10: learn: 0.0994588 total: 1.09s remaining: 26.8s 11: learn: 0.0932015 total: 1.19s 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99.99 - Accuracy_Train: 99.83 - F1-Score_Train: 99.83 - Precision_Test: 28.13 - Recall_Test: 87.30 - AUPRC_Test: 79.68 - Accuracy_Test: 99.60 - F1-Score_Test: 42.55 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 280 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.77 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5307739 total: 82ms remaining: 22.9s 1: learn: 0.3859729 total: 168ms remaining: 23.4s 2: learn: 0.2841797 total: 256ms remaining: 23.6s 3: learn: 0.2313302 total: 368ms remaining: 25.4s 4: learn: 0.1906811 total: 453ms remaining: 24.9s 5: learn: 0.1616080 total: 550ms remaining: 25.1s 6: learn: 0.1387538 total: 668ms remaining: 26.1s 7: learn: 0.1255683 total: 755ms remaining: 25.7s 8: learn: 0.1129238 total: 857ms remaining: 25.8s 9: learn: 0.1017146 total: 974ms remaining: 26.3s 10: learn: 0.0929084 total: 1.06s remaining: 25.9s 11: learn: 0.0862523 total: 1.15s remaining: 25.7s 12: learn: 0.0801182 total: 1.27s remaining: 26.2s 13: learn: 0.0765040 total: 1.35s remaining: 25.7s 14: learn: 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[I 2024-12-19 15:07:25,729] Trial 44 finished with value: 78.83109626445729 and parameters: {'learning_rate': 0.07095231087224999, 'max_depth': 6, 'n_estimators': 280, 'scale_pos_weight': 6.767127395574064}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.66 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.83 - F1-Score_Train: 99.83 - Precision_Test: 25.59 - Recall_Test: 85.71 - AUPRC_Test: 78.97 - Accuracy_Test: 99.56 - F1-Score_Test: 39.42 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 280 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.07 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.77 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 78.8311 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5047455 total: 102ms remaining: 28.9s 1: learn: 0.3434208 total: 195ms remaining: 27.7s 2: learn: 0.2526260 total: 283ms remaining: 26.7s 3: learn: 0.1916451 total: 402ms remaining: 28.3s 4: learn: 0.1527306 total: 492ms remaining: 27.7s 5: learn: 0.1240348 total: 584ms remaining: 27.3s 6: learn: 0.1027764 total: 709ms remaining: 28.3s 7: learn: 0.0855107 total: 800ms remaining: 27.8s 8: learn: 0.0767368 total: 893ms 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None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 286 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.78 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5067035 total: 94.7ms remaining: 27s 1: learn: 0.3570630 total: 198ms remaining: 28.1s 2: learn: 0.2707112 total: 292ms remaining: 27.6s 3: learn: 0.2134189 total: 442ms remaining: 31.1s 4: learn: 0.1704790 total: 536ms remaining: 30.1s 5: learn: 0.1441278 total: 626ms remaining: 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target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 286 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.78 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5075988 total: 83.6ms remaining: 23.8s 1: learn: 0.3627596 total: 188ms remaining: 26.6s 2: learn: 0.2796440 total: 272ms remaining: 25.7s 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[I 2024-12-19 15:09:06,095] Trial 45 finished with value: 79.66196040263941 and parameters: {'learning_rate': 0.08540577895279673, 'max_depth': 6, 'n_estimators': 286, 'scale_pos_weight': 5.7780633804501385}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.76 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.88 - F1-Score_Train: 99.88 - Precision_Test: 30.57 - Recall_Test: 84.92 - AUPRC_Test: 79.96 - Accuracy_Test: 99.65 - F1-Score_Test: 44.96 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 286 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.78 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 79.6620 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5686996 total: 174ms remaining: 47.4s 1: learn: 0.4436812 total: 347ms remaining: 47s 2: learn: 0.3689664 total: 520ms remaining: 46.8s 3: learn: 0.3036053 total: 677ms remaining: 45.5s 4: learn: 0.2403241 total: 865ms remaining: 46.4s 5: learn: 0.1989259 total: 1.03s remaining: 46s 6: learn: 0.1676443 total: 1.24s remaining: 47.2s 7: learn: 0.1406843 total: 1.42s remaining: 47s 8: learn: 0.1235290 total: 1.57s 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- verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 273 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.08 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando 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remaining: 116ms 272: learn: 0.0037879 total: 31.8s remaining: 0us ✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.64 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.82 - F1-Score_Train: 99.82 - Precision_Test: 27.07 - Recall_Test: 88.10 - AUPRC_Test: 80.60 - Accuracy_Test: 99.58 - F1-Score_Test: 41.42 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 273 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.08 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5705567 total: 83ms remaining: 22.6s 1: learn: 0.4495567 total: 168ms remaining: 22.8s 2: learn: 0.3681851 total: 255ms remaining: 23s 3: learn: 0.3174487 total: 363ms remaining: 24.4s 4: learn: 0.2720791 total: 446ms remaining: 23.9s 5: learn: 0.2327869 total: 529ms remaining: 23.6s 6: learn: 0.2063988 total: 636ms remaining: 24.2s 7: learn: 0.1818178 total: 720ms remaining: 23.8s 8: learn: 0.1625742 total: 798ms remaining: 23.4s 9: learn: 0.1492316 total: 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[I 2024-12-19 15:10:50,457] Trial 46 finished with value: 79.2970774657662 and parameters: {'learning_rate': 0.05325070684305075, 'max_depth': 6, 'n_estimators': 273, 'scale_pos_weight': 6.079585437760284}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.71 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.85 - F1-Score_Train: 99.85 - Precision_Test: 28.57 - Recall_Test: 85.71 - AUPRC_Test: 77.19 - Accuracy_Test: 99.62 - F1-Score_Test: 42.86 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 273 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.05 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.08 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 79.2971 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5907006 total: 94.2ms remaining: 27.2s 1: learn: 0.4833476 total: 187ms remaining: 26.9s 2: learn: 0.3934960 total: 278ms remaining: 26.6s 3: learn: 0.3280725 total: 399ms remaining: 28.6s 4: learn: 0.2757388 total: 494ms remaining: 28.2s 5: learn: 0.2381451 total: 599ms remaining: 28.4s 6: learn: 0.2071268 total: 724ms remaining: 29.3s 7: learn: 0.1779308 total: 817ms remaining: 28.8s 8: learn: 0.1578646 total: 902ms 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None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 290 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.57 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5902859 total: 93.5ms remaining: 27s 1: learn: 0.4909089 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Accuracy_Train: 99.80 - F1-Score_Train: 99.80 - Precision_Test: 25.40 - Recall_Test: 88.10 - AUPRC_Test: 76.83 - Accuracy_Test: 99.54 - F1-Score_Test: 39.43 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 290 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.57 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5921462 total: 175ms remaining: 50.7s 1: learn: 0.4935738 total: 352ms remaining: 50.7s 2: learn: 0.4124219 total: 539ms remaining: 51.6s 3: learn: 0.3708937 total: 702ms remaining: 50.2s 4: learn: 0.3201313 total: 873ms remaining: 49.7s 5: learn: 0.2830406 total: 1.04s remaining: 49.2s 6: learn: 0.2470148 total: 1.23s remaining: 49.8s 7: learn: 0.2201560 total: 1.38s remaining: 48.8s 8: learn: 0.2001463 total: 1.56s remaining: 48.7s 9: learn: 0.1820851 total: 1.67s remaining: 46.8s 10: learn: 0.1670929 total: 1.76s remaining: 44.6s 11: learn: 0.1554975 total: 1.87s remaining: 43.4s 12: learn: 0.1427949 total: 1.96s remaining: 41.7s 13: learn: 0.1342586 total: 2.04s remaining: 40.1s 14: learn: 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[I 2024-12-19 15:12:44,928] Trial 47 finished with value: 77.65157992289289 and parameters: {'learning_rate': 0.043583875498658255, 'max_depth': 6, 'n_estimators': 290, 'scale_pos_weight': 5.571176876101519}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.66 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.83 - F1-Score_Train: 99.83 - Precision_Test: 26.73 - Recall_Test: 85.71 - AUPRC_Test: 76.36 - Accuracy_Test: 99.58 - F1-Score_Test: 40.75 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 290 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.04 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 5.57 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 77.6516 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4918000 total: 105ms remaining: 26.9s 1: learn: 0.3359094 total: 194ms remaining: 24.8s 2: learn: 0.2470929 total: 285ms remaining: 24.2s 3: learn: 0.1816849 total: 416ms remaining: 26.4s 4: learn: 0.1410022 total: 507ms remaining: 25.7s 5: learn: 0.1098187 total: 598ms remaining: 25.1s 6: learn: 0.0895788 total: 711ms remaining: 25.5s 7: learn: 0.0766646 total: 803ms remaining: 25.1s 8: learn: 0.0672493 total: 901ms 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100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.85 - F1-Score_Train: 99.85 - Precision_Test: 26.49 - Recall_Test: 84.92 - AUPRC_Test: 79.33 - Accuracy_Test: 99.58 - F1-Score_Test: 40.38 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 258 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.46 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.4999932 total: 88.6ms remaining: 22.8s 1: learn: 0.3745039 total: 174ms remaining: 22.2s 2: learn: 0.2759599 total: 262ms remaining: 22.3s 3: learn: 0.2104948 total: 377ms remaining: 23.9s 4: learn: 0.1765520 total: 462ms remaining: 23.4s 5: learn: 0.1445754 total: 555ms remaining: 23.3s 6: learn: 0.1267685 total: 679ms remaining: 24.3s 7: learn: 0.1163498 total: 766ms remaining: 23.9s 8: learn: 0.1042004 total: 857ms remaining: 23.7s 9: learn: 0.0952600 total: 988ms remaining: 24.5s 10: learn: 0.0874086 total: 1.08s remaining: 24.2s 11: learn: 0.0808326 total: 1.16s remaining: 23.8s 12: learn: 0.0747277 total: 1.28s remaining: 24.1s 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eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 258 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.46 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, 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[I 2024-12-19 15:14:16,402] Trial 48 finished with value: 78.83491622763522 and parameters: {'learning_rate': 0.08763199259953189, 'max_depth': 6, 'n_estimators': 258, 'scale_pos_weight': 6.459984391212722}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.71 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.85 - F1-Score_Train: 99.85 - Precision_Test: 26.85 - Recall_Test: 86.51 - AUPRC_Test: 78.64 - Accuracy_Test: 99.58 - F1-Score_Test: 40.98 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 258 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.09 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.46 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 78.8349 🔍 Optimizando hiperparámetros para CatBoost con Optuna... 🔄 Fold 1: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5359939 total: 89.9ms remaining: 25s 1: learn: 0.3997972 total: 182ms remaining: 25.2s 2: learn: 0.3114907 total: 272ms remaining: 25.1s 3: learn: 0.2358218 total: 395ms remaining: 27.2s 4: learn: 0.1838363 total: 505ms remaining: 27.7s 5: learn: 0.1480308 total: 605ms remaining: 27.5s 6: learn: 0.1274817 total: 724ms remaining: 28.1s 7: learn: 0.1090974 total: 819ms remaining: 27.7s 8: learn: 0.0963457 total: 933ms 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Sobreajuste: 1 - Precision_Train: 99.69 - Recall_Train: 100.00 - AUPRC_Train: 99.99 - Accuracy_Train: 99.84 - F1-Score_Train: 99.85 - Precision_Test: 24.94 - Recall_Test: 85.71 - AUPRC_Test: 78.89 - Accuracy_Test: 99.54 - F1-Score_Test: 38.64 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 279 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.92 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (1, 133) 🔄 Fold 2: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: float64 📈 Después de SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando CatBoost (Optuna con SMOTE)... 0: learn: 0.5461770 total: 87.1ms remaining: 24.2s 1: learn: 0.4033238 total: 178ms remaining: 24.6s 2: learn: 0.3192476 total: 269ms remaining: 24.7s 3: learn: 0.2674701 total: 400ms remaining: 27.5s 4: learn: 0.2099489 total: 496ms remaining: 27.2s 5: learn: 0.1812987 total: 584ms remaining: 26.6s 6: learn: 0.1596767 total: 709ms remaining: 27.5s 7: learn: 0.1429312 total: 792ms remaining: 26.8s 8: learn: 0.1283402 total: 878ms remaining: 26.3s 9: learn: 0.1190589 total: 984ms remaining: 26.5s 10: learn: 0.1093645 total: 1.08s remaining: 26.3s 11: learn: 0.1002266 total: 1.18s remaining: 26.3s 12: learn: 0.0930623 total: 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99.58 - F1-Score_Test: 41.19 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 279 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.92 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (2, 133) 🔄 Fold 3: Optimización en progreso... 📊 Antes de SMOTE: Class 0 0.99831789 1 0.00168211 Name: proportion, dtype: 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[I 2024-12-19 15:16:01,997] Trial 49 finished with value: 77.89523866432027 and parameters: {'learning_rate': 0.06490773016340588, 'max_depth': 6, 'n_estimators': 279, 'scale_pos_weight': 6.917567438408559}. Best is trial 37 with value: 80.67361029056296.
✅ Resultados para CatBoost (Optuna con SMOTE): - Modelo: CatBoost - Tecnica: Optuna con SMOTE - Sobreajuste: 1 - Precision_Train: 99.66 - Recall_Train: 100.00 - AUPRC_Train: 99.98 - Accuracy_Train: 99.83 - F1-Score_Train: 99.83 - Precision_Test: 25.96 - Recall_Test: 85.71 - AUPRC_Test: 75.65 - Accuracy_Test: 99.56 - F1-Score_Test: 39.85 - model_shrink_mode: None - per_feature_ctr: None - eta: None - devices: None - verbose: None - per_object_feature_penalties: None - allow_const_label: None - mvs_reg: None - dev_score_calc_obj_block_size: None - ctr_leaf_count_limit: None - max_ctr_complexity: None - target_border: None - metric_period: None - depth: None - eval_fraction: None - allow_writing_files: None - save_snapshot: None - classes_count: None - ctr_description: None - leaf_estimation_method: None - one_hot_max_size: None - min_data_in_leaf: None - iterations: None - max_depth: 6 - random_score_type: None - colsample_bylevel: None - bootstrap_type: None - n_estimators: 279 - custom_metric: None - thread_count: None - bagging_temperature: None - random_strength: None - nan_mode: None - text_features: None - per_float_feature_quantization: None - simple_ctr: None - output_borders: None - use_best_model: None - gpu_cat_features_storage: None - combinations_ctr: None - border_count: None - feature_border_type: None - data_partition: None - fold_permutation_block: None - od_pval: None - name: None - early_stopping_rounds: None - tokenizers: None - best_model_min_trees: None - dev_efb_max_buckets: None - feature_weights: None - posterior_sampling: None - metadata: None - boosting_type: None - diffusion_temperature: None - gpu_ram_part: None - score_function: None - approx_on_full_history: None - sampling_unit: None - learning_rate: 0.06 - task_type: None - snapshot_interval: None - rsm: None - store_all_simple_ctr: None - random_seed: None - sampling_frequency: None - ctr_target_border_count: None - final_ctr_computation_mode: None - fixed_binary_splits: None - subsample: None - auto_class_weights: None - ctr_history_unit: None - device_config: None - leaf_estimation_backtracking: None - l2_leaf_reg: None - has_time: None - fold_len_multiplier: None - pinned_memory_size: None - feature_calcers: None - model_shrink_rate: None - od_type: None - monotone_constraints: None - dictionaries: None - class_weights: None - max_bin: None - boost_from_average: None - grow_policy: None - embedding_features: None - langevin: None - callback: None - cat_features: None - train_dir: None - sparse_features_conflict_fraction: None - ignored_features: None - num_trees: None - penalties_coefficient: None - objective: None - used_ram_limit: None - text_processing: None - reg_lambda: None - snapshot_file: None - random_state: None - custom_loss: None - loss_function: None - leaf_estimation_iterations: None - silent: None - max_leaves: None - input_borders: None - counter_calc_method: None - num_boost_round: None - model_size_reg: None - eval_metric: None - num_leaves: None - min_child_samples: None - class_names: None - scale_pos_weight: 6.92 - logging_level: None - first_feature_use_penalties: None - od_wait: None ✅ Tamaño del DataFrame actualizado: (3, 133) 🏆 Promedio de AUPRC en validación cruzada: 77.8952 🔍 Consolidando y ordenando resultados... 🏆 Resultados Finales Ordenados: Empty DataFrame Columns: [Modelo, Tecnica, Fold, Sobreajuste, Precision_Train, Recall_Train, AUPRC_Train, Accuracy_Train, F1-Score_Train, Precision_Test, Recall_Test, AUPRC_Test, Accuracy_Test, F1-Score_Test, learning_rate, max_depth, n_estimators, scale_pos_weight, iterations, class_weights] Index: [] ✅ Resultados guardados en 'resultados_optuna_con_smote.csv'
Consolidación de las métricas y los hiperparametros de los modelos¶
Para comparar de manera efectiva los resultados obtenidos en todas las técnicas aplicadas, consolidamos las métricas y los hiperparámetros en un único DataFrame llamado resultados_maestro.
Este proceso permite analizar el rendimiento de los modelos y las estrategias utilizadas (SMOTE, ADASYN, validación cruzada con y sin balanceo, GridSearchCV y Optuna).
In [ ]:
%%time
# Lista de DataFrames generados en las diferentes técnicas
dataframes_resultados = [
resultados_validacion_cruzada_con_smote, # Validación Cruzada con SMOTE
resultados_validacion_cruzada_sin_smote, # Validación Cruzada SIN SMOTE
resultados_adasyn, # Validación Cruzada con ADASYN
resultados_gridsearch_con_smote, # GridSearchCV con SMOTE
resultados_optuna # Optimización con Optuna
]
# Concatenar todos los resultados en un único DataFrame maestro
resultados_maestro = pd.concat(dataframes_resultados, ignore_index=True)
# Mostrar la cantidad total de resultados consolidados
print(f"✅ Consolidación completada: {len(resultados_maestro)} registros totales.")
print("\n🏆 Resultados Consolidados:")
print(resultados_maestro.head())
print(f"\n🚨 Verificación final:")
print(f"Validación Cruzada con SMOTE: {resultados_validacion_cruzada_con_smote.shape}")
print(f"Validación Cruzada SIN SMOTE: {resultados_validacion_cruzada_sin_smote.shape}")
print(f"Validación Cruzada con ADASYN: {resultados_adasyn.shape}")
print(f"GridSearchCV con SMOTE: {resultados_gridsearch_con_smote.shape}")
print(f"Optuna: {resultados_optuna.shape}")
# Guardar los resultados consolidados en un archivo CSV
output_file = "resultados_maestro_consolidados.csv"
resultados_maestro.to_csv(output_file, index=False)
print(f"\n✅ Resultados consolidados guardados en '{output_file}'")
✅ Consolidación completada: 62 registros totales.
🏆 Resultados Consolidados:
Modelo Tecnica Sobreajuste Precision_Train \
0 CatBoost Validación Cruzada con SMOTE 1 93.86611482
1 CatBoost Validación Cruzada con SMOTE 1 94.32083653
2 CatBoost Validación Cruzada con SMOTE 1 94.49939844
3 CatBoost Validación Cruzada con SMOTE 1 93.68708317
4 CatBoost Validación Cruzada con SMOTE 1 93.67646718
Recall_Train AUPRC_Train Accuracy_Train F1-Score_Train Precision_Test \
0 99.96978792 99.79652302 96.71852248 96.82185249 2.47933884
1 99.98365576 99.82593432 96.98176379 97.06972741 2.42047026
2 99.97870296 99.82816188 97.07958159 97.16186263 2.42468773
3 99.97771240 99.80735199 96.62045704 96.73023152 2.35668790
4 99.96731152 99.80694122 96.60956088 96.71970501 2.30473752
Recall_Test AUPRC_Test Accuracy_Test F1-Score_Test iterations \
0 94.73684211 67.68355898 93.68992524 4.83221477 300
1 92.10526316 60.34089979 93.70772517 4.71698113 300
2 86.84210526 63.90756485 94.06817373 4.71765547 300
3 97.36842105 71.12010920 93.17372731 4.60199005 300
4 94.73684211 62.58593364 93.20042720 4.50000000 300
learning_rate depth class_weights verbose max_depth n_estimators \
0 0.03000000 3 [1, 10] 50 None None
1 0.03000000 3 [1, 10] 50 None None
2 0.03000000 3 [1, 10] 50 None None
3 0.03000000 3 [1, 10] 50 None None
4 0.03000000 3 [1, 10] 50 None None
scale_pos_weight min_child_weight gamma l2_leaf_reg subsample Fold \
0 None NaN NaN 15.00000000 None 10
1 None NaN NaN 15.00000000 None 10
2 None NaN NaN 15.00000000 None 10
3 None NaN NaN 15.00000000 None 10
4 None NaN NaN 15.00000000 None 10
model_shrink_mode per_feature_ctr eta devices \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
per_object_feature_penalties allow_const_label mvs_reg \
0 None None None
1 None None None
2 None None None
3 None None None
4 None None None
dev_score_calc_obj_block_size ctr_leaf_count_limit max_ctr_complexity \
0 None None None
1 None None None
2 None None None
3 None None None
4 None None None
target_border metric_period eval_fraction allow_writing_files save_snapshot \
0 None None None None None
1 None None None None None
2 None None None None None
3 None None None None None
4 None None None None None
classes_count ctr_description leaf_estimation_method one_hot_max_size \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
min_data_in_leaf random_score_type colsample_bylevel bootstrap_type \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
custom_metric thread_count bagging_temperature random_strength nan_mode \
0 None None None None None
1 None None None None None
2 None None None None None
3 None None None None None
4 None None None None None
text_features per_float_feature_quantization simple_ctr output_borders \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
use_best_model gpu_cat_features_storage combinations_ctr border_count \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
feature_border_type data_partition fold_permutation_block od_pval name \
0 None None None None None
1 None None None None None
2 None None None None None
3 None None None None None
4 None None None None None
early_stopping_rounds tokenizers best_model_min_trees dev_efb_max_buckets \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
feature_weights posterior_sampling metadata boosting_type \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
diffusion_temperature gpu_ram_part score_function approx_on_full_history \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
sampling_unit task_type snapshot_interval rsm store_all_simple_ctr \
0 None None None None None
1 None None None None None
2 None None None None None
3 None None None None None
4 None None None None None
random_seed sampling_frequency ctr_target_border_count \
0 None None None
1 None None None
2 None None None
3 None None None
4 None None None
final_ctr_computation_mode fixed_binary_splits auto_class_weights \
0 None None None
1 None None None
2 None None None
3 None None None
4 None None None
ctr_history_unit device_config leaf_estimation_backtracking has_time \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
fold_len_multiplier pinned_memory_size feature_calcers model_shrink_rate \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
od_type monotone_constraints dictionaries max_bin boost_from_average \
0 None None None None None
1 None None None None None
2 None None None None None
3 None None None None None
4 None None None None None
grow_policy embedding_features langevin callback cat_features train_dir \
0 None None None None None None
1 None None None None None None
2 None None None None None None
3 None None None None None None
4 None None None None None None
sparse_features_conflict_fraction ignored_features num_trees \
0 None None None
1 None None None
2 None None None
3 None None None
4 None None None
penalties_coefficient objective used_ram_limit text_processing reg_lambda \
0 None None None None None
1 None None None None None
2 None None None None None
3 None None None None None
4 None None None None None
snapshot_file random_state custom_loss loss_function \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
leaf_estimation_iterations silent max_leaves input_borders \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
counter_calc_method num_boost_round model_size_reg eval_metric num_leaves \
0 None None None None None
1 None None None None None
2 None None None None None
3 None None None None None
4 None None None None None
min_child_samples class_names logging_level first_feature_use_penalties \
0 None None None None
1 None None None None
2 None None None None
3 None None None None
4 None None None None
od_wait kwargs
0 None NaN
1 None NaN
2 None NaN
3 None NaN
4 None NaN
🚨 Verificación final:
Validación Cruzada con SMOTE: (20, 136)
Validación Cruzada SIN SMOTE: (20, 136)
Validación Cruzada con ADASYN: (20, 136)
GridSearchCV con SMOTE: (2, 134)
Optuna: (0, 20)
✅ Resultados consolidados guardados en 'resultados_maestro_consolidados.csv'
CPU times: user 90.4 ms, sys: 1.99 ms, total: 92.4 ms
Wall time: 94.8 ms
Seleccionar el Modelo Ganador¶
Entrenar y Guardar el Modelo Ganador en .pkl:
Identifica Modelo Ganador, extrae hiperarámetros dinámicamente y instancia, entrena y guarda el .pkl Automáticamente.
In [ ]:
resultados_Sobreajuste = resultados_maestro[resultados_maestro['Sobreajuste'] == 1]
resultados_Sobreajuste.shape
Out[ ]:
(52, 136)
In [ ]:
resultados_NoSobreajuste = resultados_maestro[resultados_maestro['Sobreajuste'] == 0]
resultados_NoSobreajuste.shape
Out[ ]:
(10, 136)
In [ ]:
# ============================
# Filtrar y Ordenar el Modelo Ganador
# ============================
from sklearn.model_selection import StratifiedKFold
from imblearn.over_sampling import SMOTE, ADASYN
import joblib
from inspect import signature
# Diccionario de clases de modelos
model_classes = {
"CatBoost": CatBoostClassifier,
"XGBoost": XGBClassifier
}
# Paso 1: Filtrar modelos sin sobreajuste
resultados_filtrados = resultados_maestro[resultados_maestro['Sobreajuste'] == 0]
# Verificar si hay modelos válidos después del filtro
if resultados_filtrados.empty:
print("\n❌ No se encontraron modelos válidos después de aplicar los filtros de sobreajuste.")
else:
# Paso 2: Filtrar modelos con Recall y AUPRC estrictamente menores a 99.99%
resultados_filtrados = resultados_filtrados[
(resultados_filtrados['Recall_Test'] < 99.99) & (resultados_filtrados['AUPRC_Test'] < 99.99)
]
if resultados_filtrados.empty:
print("\n❌ No se encontraron modelos después de aplicar filtros de métricas (Recall/AUPRC).")
else:
# Paso 3: Ordenar por Recall, AUPRC, Precision y F1-Score en orden descendente
resultados_ordenados = resultados_filtrados.sort_values(
by=['Recall_Test', 'AUPRC_Test', 'Precision_Test', 'F1-Score_Test'],
ascending=[False, False, False, False]
)
# Seleccionar el modelo top 1
mejor_modelo = resultados_ordenados.iloc[0]
modelo_tipo = mejor_modelo['Modelo']
tecnica_ganadora = mejor_modelo['Tecnica']
# ============================
# Extraer Hiperparámetros del Modelo Ganador
# ============================
hiperparametros_mejor_modelo = mejor_modelo.dropna()
columnas_excluir = [
'Modelo', 'Tecnica', 'Sobreajuste', 'Recall_Train', 'AUPRC_Train',
'Precision_Train', 'F1-Score_Train', 'Accuracy_Train',
'Recall_Test', 'AUPRC_Test', 'Precision_Test', 'F1-Score_Test', 'Accuracy_Test'
]
hiperparametros_final = {
col: val for col, val in hiperparametros_mejor_modelo.items()
if col not in columnas_excluir and not isinstance(val, (list, np.ndarray))
}
parametros_validos = set(signature(model_classes[modelo_tipo]).parameters.keys())
hiperparametros_final = {k: v for k, v in hiperparametros_final.items() if k in parametros_validos}
# ============================
# Imprimir Resumen del Modelo Ganador
# ============================
print("\n\033[1m🏆 **Modelo Ganador Seleccionado:**\033[0m")
print(f"🔹 \033[1mModelo:\033[0m {modelo_tipo}")
print(f"🔹 \033[1mTécnica Ganadora:\033[0m {tecnica_ganadora}")
print(f"🔹 \033[1mRecall (Test):\033[0m {mejor_modelo['Recall_Test']:.2f}%")
print(f"🔹 \033[1mAUPRC (Test):\033[0m {mejor_modelo['AUPRC_Test']:.2f}%")
print(f"🔹 \033[1mPrecision (Test):\033[0m {mejor_modelo['Precision_Test']:.2f}%")
print(f"🔹 \033[1mF1-Score (Test):\033[0m {mejor_modelo['F1-Score_Test']:.2f}%")
print("\n\033[1m🔧 **Hiperparámetros del Modelo Ganador:**\033[0m")
for param, value in hiperparametros_final.items():
print(f"🔹 \033[1m{param}:\033[0m {value}")
# ============================
# Entrenamiento del Modelo Ganador
# ============================
print("\n🔧 Instanciando el modelo ganador...")
modelo_ganador = model_classes[modelo_tipo](**hiperparametros_final)
if "SMOTE" in tecnica_ganadora or "ADASYN" in tecnica_ganadora:
print(f"\n🚀 Aplicando {tecnica_ganadora} al conjunto completo...")
smote_adasyn = SMOTE(random_state=42) if "SMOTE" in tecnica_ganadora else ADASYN(random_state=42)
X_res, y_res = smote_adasyn.fit_resample(X_train, y_train)
print(f"📊 Distribución de clases después de {tecnica_ganadora}:")
print(y_res.value_counts(normalize=True))
print("\n🚀 Entrenando el modelo final...")
modelo_ganador.fit(X_res, y_res)
elif "SIN SMOTE" in tecnica_ganadora:
print("\n🚀 Entrenando el modelo final SIN SMOTE...")
modelo_ganador.fit(X_train, y_train)
elif "GridSearchCV" in tecnica_ganadora or "Optuna" in tecnica_ganadora:
print("\n🚀 Entrenando el modelo final con hiperparámetros optimizados...")
modelo_ganador.fit(X_train, y_train)
else:
print("\n⚠️ Técnica desconocida. Entrenando con el conjunto completo...")
modelo_ganador.fit(X_train, y_train)
# ============================
# Guardar el Modelo Ganador
# ============================
nombre_modelo_pkl = f"{modelo_tipo.lower()}_final.pkl"
joblib.dump(modelo_ganador, nombre_modelo_pkl)
print(f"\n✅ Modelo guardado como: {nombre_modelo_pkl}")
🏆 **Modelo Ganador Seleccionado:** 🔹 Modelo: XGBoost 🔹 Técnica Ganadora: Validación Cruzada SIN SMOTE 🔹 Recall (Test): 89.19% 🔹 AUPRC (Test): 95.20% 🔹 Precision (Test): 94.29% 🔹 F1-Score (Test): 91.67% 🔧 **Hiperparámetros del Modelo Ganador:** 🔧 Instanciando el modelo ganador... 🚀 Aplicando Validación Cruzada SIN SMOTE al conjunto completo... 📊 Distribución de clases después de Validación Cruzada SIN SMOTE: Class 0 0.50000000 1 0.50000000 Name: proportion, dtype: float64 🚀 Entrenando el modelo final... ✅ Modelo guardado como: xgboost_final.pkl
Cargar el Modelo Más Reciente¶
Busca y carga el "_final.pkl" más reciente en la raiz del proyecto de forma automática.
In [ ]:
# ==========================
# Cargar el Modelo Más Reciente
# ==========================
# Buscar archivos .pkl y seleccionar el más reciente
pkl_files = [f for f in os.listdir() if f.endswith("_final.pkl")]
# Verificar si existen archivos pkl
if not pkl_files:
raise FileNotFoundError("No se encontraron archivos .pkl en el directorio actual.")
# Ordenar archivos por fecha de modificación
pkl_files.sort(key=os.path.getmtime, reverse=True)
modelo_pkl = pkl_files[0] # Seleccionar el archivo más reciente
# Cargar el modelo
print(f"🔍 Modelo más reciente cargado: {modelo_pkl}")
modelo_ganador = joblib.load(modelo_pkl)
🔍 Modelo más reciente cargado: xgboost_final.pkl
Realizar Predicciones¶
Realiza las predicciones en el conjunto de prueba (X_Test).
In [ ]:
# ==========================
# Realizar Predicciones
# ==========================
print("\n🚀 Realizando predicciones en el conjunto de prueba...")
y_pred = modelo_ganador.predict(X_test)
y_pred_proba = modelo_ganador.predict_proba(X_test)[:, 1] # Probabilidades clase positiva
# Comparación de las primeras 20 predicciones
print("\n📊 Comparación de Predicciones:")
comparacion_df = pd.DataFrame({
"Categorías Reales": y_test.values[:20],
"Categorías Predichas": y_pred[:20]
})
print(comparacion_df)
🚀 Realizando predicciones en el conjunto de prueba...
📊 Comparación de Predicciones:
Categorías Reales Categorías Predichas
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 0 0
11 0 0
12 0 0
13 0 0
14 0 0
15 0 0
16 0 0
17 0 0
18 0 0
19 0 0
Informe de Clasificación y Métricas¶
Presenta el Informe de Clasificación y Métricas con el resultado de las predicciones.
Visualizaciones
Genera la Matriz de Confusión, la Curva Precision-Recall y la Curva-Roc para el conjunto de prueba (X_test)
In [ ]:
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
import matplotlib.pyplot as plt
# ============================
# Generar y Visualizar Matriz de Confusión
# ============================
print("\n📊 Visualizando la Matriz de Confusión...")
# Generar predicciones del modelo ganador
y_pred = modelo_ganador.predict(X_test)
# Calcular matriz de confusión
cm = confusion_matrix(y_test, y_pred)
# Visualizar la matriz de confusión
fig, ax = plt.subplots(figsize=(8, 6))
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["Clase 0", "Clase 1"])
disp.plot(cmap='Blues', ax=ax)
ax.set_title(f"Matriz de Confusión - {modelo_tipo}")
plt.show()
Aplicación para probar el modelo con datos nuevos¶
Aplicación para probar el modelo con datos nuevos generados aleatoriamente.
- Carga automáticamente el archivo .pkl más reciente encontrado en la carpeta. De esta manera, el modelo con fecha de última modificación más actualizada será seleccionado.
Generar datos simulados en un archivo CSV¶
Genera datos aleatorios consistentes con las características (features) de tu dataset original, como la variable Amount escalada y otras columnas que el modelo utiliza.
In [ ]:
# =======================================
# Generar datos simulados y exportar a CSV
# =======================================
from google.colab import files
# Configurar parámetros
n_muestras = 50 # Número de muestras aleatorias a generar
np.random.seed(42) # Fijar la semilla para reproducibilidad
# Crear la estructura con las mismas columnas y orden del dataframe clean_data
columnas_originales = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9',
'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17',
'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25',
'V26', 'V27', 'V28', 'Amount'] # Ajusta según clean_data
# Generar valores aleatorios para cada columna
data_simulada = pd.DataFrame({
col: np.random.uniform(-5, 5, n_muestras) if col != 'Amount' else np.random.uniform(0, 2000, n_muestras)
for col in columnas_originales
})
# =============================
# Guardar los datos simulados
# =============================
output_csv = "datos_nuevos_simulados.csv"
data_simulada.to_csv(output_csv, index=False)
print(f"✅ Archivo generado y guardado como '{output_csv}'")
# =============================
# Visualizar los datos
# =============================
print("\n📊 Primeras filas de los datos generados:")
display(data_simulada.head(5))
# =============================
# Descargar el archivo CSV
# =============================
files.download(output_csv) # Descargar automáticamente el archivo generado en tu ordenador
| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.25459881 | 4.69584628 | -4.68570814 | 4.08265886 | 1.42031646 | -2.05551108 | -4.48318279 | 0.03136259 | -3.96876131 | 2.77146916 | 1.98161714 | 0.20163501 | -3.31064937 | -4.13079712 | 0.32589433 | -3.83101959 | 2.07238634 | -4.32923523 | -2.92113745 | 1.00516860 | -3.14867071 | 3.74701673 | -4.42157323 | 2.08362977 | 2.58263196 | 2.96537291 | -4.68413855 | 3.67031496 | 334.08381580 |
| 1 | 4.50714306 | 2.75132823 | 1.36410411 | -2.60438109 | -4.15860035 | -1.14902271 | 0.31354632 | 3.56489841 | 4.02552907 | 0.58404250 | 0.36096366 | 3.52181500 | -2.21409661 | 0.37106542 | -4.48176463 | 4.39832124 | -3.47460957 | 2.49960470 | -4.73467796 | 1.65036675 | 0.41900947 | 0.28937134 | 4.69102630 | 3.37013328 | -4.75413084 | -0.18477648 | 4.36212246 | 0.78675409 | 335.23843258 |
| 2 | 2.31993942 | 4.39498942 | -1.85644019 | -3.55105128 | -3.38371286 | 3.51136672 | 0.40635122 | 1.58693632 | 0.05252372 | -0.75777991 | -1.90472384 | 0.51906839 | -3.22989516 | 0.86841118 | -1.63395722 | 1.27708053 | 0.76288360 | -2.90094407 | -3.18564565 | -3.24628721 | 3.72945836 | 4.39067699 | 3.83785885 | 1.97471462 | -4.77876448 | -3.82691811 | -4.48028716 | -0.61384581 | 73.34285387 |
| 3 | 0.98658484 | 3.94827350 | 0.08570691 | -0.10547240 | 3.98554189 | -1.83077995 | 1.37429901 | -3.37065573 | 3.26457466 | 4.06354385 | 3.13795020 | 0.60937972 | -4.11297466 | 2.45439474 | -3.65585323 | -1.65094385 | 1.06715046 | 3.98054289 | 0.83041561 | 4.14411946 | 2.32224886 | 2.98783236 | 4.27752283 | 1.80140772 | -1.76389781 | -3.74814208 | 0.41296335 | 2.25257660 | 1472.80403013 |
| 4 | -3.43981360 | 0.97899979 | 4.07566474 | 4.85650454 | 1.06429060 | -3.30507253 | 2.26091334 | -4.29431253 | -1.79950399 | -3.88802518 | 1.84731173 | 3.76653603 | -3.79364129 | -0.68340454 | -4.36625030 | -3.60727927 | -0.75869329 | -2.94860360 | -0.78575449 | -0.81229475 | 3.06561148 | 4.97934111 | 4.94907823 | 1.18611378 | -0.11356810 | 1.85565287 | 2.09060519 | -0.13331059 | 1327.60905524 |
Predicción interactiva : Importar CSV y predecir nuevos datos¶
Cargar el archivo CSV con datos nuevos generados en el paso anterior y generar predicciones usando tu modelo .pkl
- El archivo .CSV debe estar descargado en una carpeta de su ordenador, normalmente se genera directamente en la carpeta de Descargas.
In [ ]:
# ============================
# 1. Configurar Columnas Esperadas
# ============================
columnas_originales = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9',
'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17',
'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25',
'V26', 'V27', 'V28', 'Amount']
# ============================
# 2. Subir el archivo CSV
# ============================
print("🔄 Suba su archivo CSV con los datos para predecir:")
uploaded = files.upload()
# ============================
# 3. Validar el archivo cargado y su estructura
# ============================
for filename in uploaded.keys():
try:
print(f"\n✅ Archivo cargado: {filename}")
data_nueva = pd.read_csv(filename)
# Validar si el archivo contiene todas las columnas esperadas
columnas_faltantes = [col for col in columnas_originales if col not in data_nueva.columns]
if columnas_faltantes:
raise ValueError(f"❌ Faltan las siguientes columnas en el archivo: {columnas_faltantes}")
# Reordenar las columnas para asegurar la consistencia
data_nueva = data_nueva[columnas_originales]
# Mostrar las primeras filas después de la validación
print("\n📊 Primeras filas del archivo validado:")
display(data_nueva.head(3)) # Mostrar 3 primeras líneas
# ============================
# 4. Cargar el modelo ganador
# ============================
try:
modelos_pkl = [f for f in os.listdir() if f.endswith("_final.pkl")]
if not modelos_pkl: # Verificar si la lista está vacía
raise FileNotFoundError("❌ No se encontró ningún archivo '_final.pkl' en el directorio.")
modelo_pkl = modelos_pkl[0] # Tomar el primer archivo encontrado
print(f"\n🔍 Cargando el modelo: {modelo_pkl}")
modelo_ganador = joblib.load(modelo_pkl)
except FileNotFoundError as fnf_error:
print(fnf_error)
print("⚠️ Asegúrese de que el modelo esté guardado con el nombre correcto en el directorio.")
raise
# ============================
# 5. Realizar predicciones
# ============================
print("\n🚀 Realizando predicciones con el modelo ganador...")
predicciones = modelo_ganador.predict(data_nueva)
# ============================
# 6. Añadir predicciones al DataFrame
# ============================
data_nueva['Class'] = predicciones # Añadir columna con predicciones
# Mostrar resultados
print("\n📈 Resultados de la predicción (con columna 'Class'):")
display(data_nueva.head(3)) # Mostrar 3 primeras líneas
# ============================
# 7. Resumen de las predicciones
# ============================
num_fraude = (data_nueva['Class'] == 1).sum()
num_no_fraude = (data_nueva['Class'] == 0).sum()
print("\n🔍 Resumen de Predicciones:")
print(f"🔸 Transacciones predichas como FRAUDE (1): {num_fraude}")
print(f"🔸 Transacciones predichas como NO FRAUDE (0): {num_no_fraude}")
# ============================
# 8. Guardar y Descargar el archivo con predicciones
# ============================
output_filename = "predicciones_resultados.csv"
data_nueva.to_csv(output_filename, index=False)
print(f"\n✅ Archivo con predicciones guardado como '{output_filename}'")
files.download(output_filename) # Descargar archivo
except Exception as e:
print(f"❌ Error: {e}")
🔄 Suba su archivo CSV con los datos para predecir:
Saving datos_nuevos_simulados.csv to datos_nuevos_simulados (1).csv ✅ Archivo cargado: datos_nuevos_simulados (1).csv 📊 Primeras filas del archivo validado:
| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.25459881 | 4.69584628 | -4.68570814 | 4.08265886 | 1.42031646 | -2.05551108 | -4.48318279 | 0.03136259 | -3.96876131 | 2.77146916 | 1.98161714 | 0.20163501 | -3.31064937 | -4.13079712 | 0.32589433 | -3.83101959 | 2.07238634 | -4.32923523 | -2.92113745 | 1.00516860 | -3.14867071 | 3.74701673 | -4.42157323 | 2.08362977 | 2.58263196 | 2.96537291 | -4.68413855 | 3.67031496 | 334.08381580 |
| 1 | 4.50714306 | 2.75132823 | 1.36410411 | -2.60438109 | -4.15860035 | -1.14902271 | 0.31354632 | 3.56489841 | 4.02552907 | 0.58404250 | 0.36096366 | 3.52181500 | -2.21409661 | 0.37106542 | -4.48176463 | 4.39832124 | -3.47460957 | 2.49960470 | -4.73467796 | 1.65036675 | 0.41900947 | 0.28937134 | 4.69102630 | 3.37013328 | -4.75413084 | -0.18477648 | 4.36212246 | 0.78675409 | 335.23843258 |
| 2 | 2.31993942 | 4.39498942 | -1.85644019 | -3.55105128 | -3.38371286 | 3.51136672 | 0.40635122 | 1.58693632 | 0.05252372 | -0.75777991 | -1.90472384 | 0.51906839 | -3.22989516 | 0.86841118 | -1.63395722 | 1.27708053 | 0.76288360 | -2.90094407 | -3.18564565 | -3.24628721 | 3.72945836 | 4.39067699 | 3.83785885 | 1.97471462 | -4.77876448 | -3.82691811 | -4.48028716 | -0.61384581 | 73.34285387 |
🔍 Cargando el modelo: xgboost_final.pkl 🚀 Realizando predicciones con el modelo ganador... 📈 Resultados de la predicción (con columna 'Class'):
| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.25459881 | 4.69584628 | -4.68570814 | 4.08265886 | 1.42031646 | -2.05551108 | -4.48318279 | 0.03136259 | -3.96876131 | 2.77146916 | 1.98161714 | 0.20163501 | -3.31064937 | -4.13079712 | 0.32589433 | -3.83101959 | 2.07238634 | -4.32923523 | -2.92113745 | 1.00516860 | -3.14867071 | 3.74701673 | -4.42157323 | 2.08362977 | 2.58263196 | 2.96537291 | -4.68413855 | 3.67031496 | 334.08381580 | 1 |
| 1 | 4.50714306 | 2.75132823 | 1.36410411 | -2.60438109 | -4.15860035 | -1.14902271 | 0.31354632 | 3.56489841 | 4.02552907 | 0.58404250 | 0.36096366 | 3.52181500 | -2.21409661 | 0.37106542 | -4.48176463 | 4.39832124 | -3.47460957 | 2.49960470 | -4.73467796 | 1.65036675 | 0.41900947 | 0.28937134 | 4.69102630 | 3.37013328 | -4.75413084 | -0.18477648 | 4.36212246 | 0.78675409 | 335.23843258 | 0 |
| 2 | 2.31993942 | 4.39498942 | -1.85644019 | -3.55105128 | -3.38371286 | 3.51136672 | 0.40635122 | 1.58693632 | 0.05252372 | -0.75777991 | -1.90472384 | 0.51906839 | -3.22989516 | 0.86841118 | -1.63395722 | 1.27708053 | 0.76288360 | -2.90094407 | -3.18564565 | -3.24628721 | 3.72945836 | 4.39067699 | 3.83785885 | 1.97471462 | -4.77876448 | -3.82691811 | -4.48028716 | -0.61384581 | 73.34285387 | 0 |
🔍 Resumen de Predicciones: 🔸 Transacciones predichas como FRAUDE (1): 31 🔸 Transacciones predichas como NO FRAUDE (0): 19 ✅ Archivo con predicciones guardado como 'predicciones_resultados.csv'
Visualizaciones¶
In [ ]:
# Muestra la distribución de las transacciones fraudulentas con respecto de las
# no fraudulentas
#colors = ["blue", "red"]
colors = ["#87CEEB", "#FF6347"]
# Configurar la visualización en una fila
fig, axes = plt.subplots(1, figsize=(10, 6))
# Función para agregar etiquetas dentro de las barras
def add_labels(ax):
for p in ax.patches:
ax.annotate(f'{int(p.get_height())}', (p.get_x() + p.get_width() / 2,
p.get_height()),
ha='center', va='bottom', fontsize=12, color='black',
weight='bold')
# Cuenta las veces que ocurre cada clase (0: no fraude, 1: fraude)
ax1 = sns.countplot(x='Class', data=data_nueva, palette=colors)
ax1.set_title('Transaciones fraudulentas vs no fraudulentas', fontsize=14,
fontweight='bold')
ax1.set_xlabel("Tipo de Transacción")
ax1.set_ylabel("Cantidad de transacciones")
add_labels(ax1)
plt.xticks([0, 1], ['No-Fraude', 'Fraude'])
plt.show()
Conclusiones¶
📊 Conclusiones del Análisis de Modelos¶
Objetivo del Proyecto: Detectar transacciones fraudulentas en un conjunto de datos desbalanceado mediante modelos de Machine Learning y técnicas avanzadas de optimización.
🔎 Estrategias Aplicadas en el Proyecto¶
Se implementaron técnicas específicas para abordar los desafíos del desbalance y maximizar el rendimiento de los modelos XGBoost y CatBoost.
Tratamiento del Desbalance de Datos: SMOTE
- El dataset original presentaba un desbalance significativo, con muy pocos registros de fraude en comparación con transacciones no fraudulentas.
- SMOTE (Synthetic Minority Over-sampling Technique):
- Aplicado exclusivamente al conjunto de entrenamiento para generar ejemplos sintéticos de la clase minoritaria (fraude).
- Esto permitió al modelo aprender patrones más robustos de la clase minoritaria sin eliminar información, como ocurriría con undersampling.
- Razón para no usar el dataset original:
Entrenar sin ninguna técnica de balanceo habría sesgado el modelo hacia la clase mayoritaria, resultando en un Recall bajo para los fraudes.
Validación Cruzada (Stratified K-Fold):
- Se aplicó una división en 5 pliegues para evaluar de manera robusta el rendimiento de los modelos, asegurando que la distribución de clases fuera equilibrada en cada pliegue.
- Esta técnica ayudó a evitar que los resultados dependieran de una única división del dataset, obteniendo métricas más generalizables.
Optimización de Hiperparámetros:
- Se utilizaron dos técnicas avanzadas para afinar los hiperparámetros:
- GridSearchCV: Exploró combinaciones predefinidas de hiperparámetros de manera exhaustiva.
- Optuna: Automatizó la búsqueda mediante una optimización bayesiana eficiente, reduciendo tiempos de cómputo.
- Se utilizaron dos técnicas avanzadas para afinar los hiperparámetros:
🛠️ Desafíos Abordados¶
- Desbalance del Dataset: Resuelto mediante SMOTE aplicado al conjunto de entrenamiento.
- Validación Generalizada: Garantizada mediante Validación Cruzada.
- Ajuste de Hiperparámetros: Optimizados mediante GridSearchCV y Optuna.
- Comparación de Algoritmos: Evaluación de XGBoost y CatBoost en métricas clave (Recall, Precision, AUPRC).
⚠️ Consideraciones sobre la Métrica Accuracy¶
Aunque la Accuracy del modelo ganador fue del 99.62%, esta métrica no es adecuada en datasets desbalanceados, como es el caso de fraudes.
- Un modelo que predijera todo como "No Fraude" tendría una Accuracy alta, pero su capacidad de identificar fraudes sería nula.
- Por esta razón, se priorizaron Recall y AUPRC, métricas que son más relevantes para evaluar el rendimiento en la clase minoritaria (fraude).
🚀 Próximos Pasos y Mejoras Futuras¶
Debido a limitaciones de tiempo, no se implementaron algunas estrategias adicionales que podrían mejorar aún más el rendimiento del modelo:
- Feature Engineering Avanzado: Crear características nuevas y más informativas que ayuden a detectar patrones complejos.
- Optimización del Umbral de Clasificación: Ajustar el umbral de decisión para equilibrar Recall y Precision.
- Despliegue del Modelo: Implementar el modelo en producción y realizar pruebas con datos en tiempo real.
Generar informe dinámico en formato PDF del modelo ganador¶
Este código crea un informe dinámico en formato PDF con contenido basado en tus datos y resultados.
In [ ]:
%%capture output
import pandas as pd
from weasyprint import HTML
from datetime import datetime
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns
# =============================
# 1. Extraer Datos Dinámicos
# =============================
# Supongamos que resultados_maestro es tu DataFrame final
best_model = resultados_maestro.loc[resultados_maestro["AUPRC_Test"].idxmax()]
# Extraer valores del modelo ganador
best_model_name = best_model["Modelo"]
best_technique = best_model["Tecnica"]
recall = best_model["Recall_Test"]
precision = best_model["Precision_Test"]
auprc = best_model["AUPRC_Test"]
f1_score = best_model["F1-Score_Test"]
accuracy = best_model["Accuracy_Test"]
# =============================
# 2. Generar Gráficos
# =============================
# Gráfico de comparación de modelos
plt.figure(figsize=(10, 6))
sns.barplot(x=resultados_maestro["Modelo"], y=resultados_maestro["AUPRC_Test"], palette="viridis")
plt.title("Comparación de Modelos: AUPRC")
plt.ylabel("AUPRC (%)")
plt.xlabel("Modelo")
plt.tight_layout()
plt.savefig("comparacion_modelos.png") # Guardar gráfico
plt.close()
# =============================
# 3. Crear el Contenido del Informe
# =============================
html_content = f"""
<!DOCTYPE html>
<html lang="es">
<head>
<meta charset="UTF-8">
<title>Informe Final - Proyecto de Detección de Fraude</title>
<style>
body {{ font-family: Arial, sans-serif; margin: 20px; }}
h1, h2 {{ color: #2E4053; }}
table {{ border-collapse: collapse; width: 100%; }}
th, td {{ border: 1px solid #ddd; padding: 8px; text-align: center; }}
th {{ background-color: #4CAF50; color: white; }}
img {{ display: block; margin: 20px auto; max-width: 80%; }}
ul {{ margin: 10px; }}
li {{ margin: 5px 0; }}
</style>
</head>
<body>
<h1>Informe Final - Proyecto de Detección de Fraude</h1>
<p>Fecha de Generación: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
<h2>🏆 Modelo Ganador: {best_model_name} ({best_technique})</h2>
<table>
<tr>
<th>Métrica</th><th>Valor</th>
</tr>
<tr><td>Recall</td><td>{recall:.2f}%</td></tr>
<tr><td>Precision</td><td>{precision:.2f}%</td></tr>
<tr><td>AUPRC</td><td>{auprc:.2f}%</td></tr>
<tr><td>F1-Score</td><td>{f1_score:.2f}%</td></tr>
<tr><td>Accuracy</td><td>{accuracy:.2f}%</td></tr>
</table>
<h2>Comparación de Modelos</h2>
<img src="comparacion_modelos.png" alt="Comparación de Modelos">
<h2>Conclusión</h2>
<p>
El modelo <strong>{best_model_name}</strong> optimizado mediante
<strong>{best_technique}</strong> es el más adecuado para la detección de fraudes
debido a su equilibrio en las métricas clave.
</p>
<h2>Próximos Pasos</h2>
<ul>
<li>Optimización del umbral de clasificación.</li>
<li>Despliegue del modelo en producción.</li>
<li>Monitoreo continuo del rendimiento del modelo.</li>
</ul>
</body>
</html>
"""
# =============================
# 4. Guardar el HTML y Convertirlo a PDF
# =============================
# Guardar el contenido en un archivo HTML
html_filename = "informe_final.html"
with open(html_filename, "w", encoding="utf-8") as f:
f.write(html_content)
# Convertir el HTML a PDF
pdf_filename = "informe_final.pdf"
HTML(html_filename).write_pdf(pdf_filename)
# Mensaje de confirmación
print(f"✅ Informe PDF generado exitosamente: {pdf_filename}")
from google.colab import files
# Descargar automáticamente el archivo PDF
files.download(pdf_filename)
Exportar cuaderno a formato HTML y a formato PDF¶
Descarga el archivo generado directamente desde Colab¶
In [17]:
import json
import shutil
from google.colab import files
# Función para limpiar y validar el notebook
def fix_notebook_metadata(filepath):
with open(filepath, 'r', encoding='utf-8') as file:
notebook = json.load(file)
for cell in notebook.get('cells', []):
if 'outputs' in cell:
for output in cell['outputs']:
# Agregar 'metadata' si no existe
if 'metadata' not in output:
output['metadata'] = {}
# Guardar el notebook limpio
with open(filepath, 'w', encoding='utf-8') as file:
json.dump(notebook, file, indent=2, ensure_ascii=False)
# Rutas de archivos
notebook_path = "/content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.ipynb"
html_output_path = "/content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.html"
pdf_output_path_viahtml = "/content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim-pdfviahtml.pdf"
# Corregir el notebook
print("\nCorrigiendo y validando el notebook...")
fix_notebook_metadata(notebook_path)
# Exportar a HTML
print("\nExportando a formato HTML...")
!jupyter nbconvert --to html --output "$html_output_path" "$notebook_path"
# Exportar a PDF usando PDFviaHTML
print("\nExportando a formato PDF usando nbconvert con PDFviaHTML...")
!jupyter nbconvert --to pdfviahtml --output "$pdf_output_path_viahtml" "$notebook_path"
# Descargar archivos generados
print("\nDescargando los archivos generados...")
try:
files.download(html_output_path) # Descargar HTML
files.download(pdf_output_path_viahtml) # Descargar PDF generado con PDFviaHTML
except Exception as e:
print(f"Error descargando los archivos: {e}")
print("\nExportación y descarga completadas.")
Limpiando el notebook...
Exportando a formato HTML...
[NbConvertApp] Converting notebook /content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.ipynb to html
[NbConvertApp] ERROR | Notebook JSON is invalid: 'metadata' is a required property
Failed validating 'required' in display_data:
On instance['cells'][20]['outputs'][0]:
{'data': {'text/html': '<style>.container { width:100% !important; }</style>',
'text/plain': '<IPython.core.display.HTML object>'},
'output_type': 'display_data'}
[NbConvertApp] ERROR | Notebook is invalid after preprocessor <nbconvert.preprocessors.tagremove.TagRemovePreprocessor object at 0x7a0a9dfb5060>
Traceback (most recent call last):
File "/usr/local/bin/jupyter-nbconvert", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.10/dist-packages/jupyter_core/application.py", line 283, in launch_instance
super().launch_instance(argv=argv, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/usr/local/lib/python3.10/dist-packages/nbconvert/nbconvertapp.py", line 420, in start
self.convert_notebooks()
File "/usr/local/lib/python3.10/dist-packages/nbconvert/nbconvertapp.py", line 597, in convert_notebooks
self.convert_single_notebook(notebook_filename)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/nbconvertapp.py", line 563, in convert_single_notebook
output, resources = self.export_single_notebook(
File "/usr/local/lib/python3.10/dist-packages/nbconvert/nbconvertapp.py", line 487, in export_single_notebook
output, resources = self.exporter.from_filename(
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/templateexporter.py", line 386, in from_filename
return super().from_filename(filename, resources, **kw) # type:ignore[return-value]
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 201, in from_filename
return self.from_file(f, resources=resources, **kw)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/templateexporter.py", line 392, in from_file
return super().from_file(file_stream, resources, **kw) # type:ignore[return-value]
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 220, in from_file
return self.from_notebook_node(
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/html.py", line 268, in from_notebook_node
html, resources = super().from_notebook_node(nb, resources, **kw)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/templateexporter.py", line 408, in from_notebook_node
nb_copy, resources = super().from_notebook_node(nb, resources, **kw)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 154, in from_notebook_node
nb_copy, resources = self._preprocess(nb_copy, resources)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 355, in _preprocess
self._validate_preprocessor(nbc, preprocessor)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 322, in _validate_preprocessor
nbformat.validate(nbc, relax_add_props=True)
File "/usr/local/lib/python3.10/dist-packages/nbformat/validator.py", line 509, in validate
raise error
nbformat.validator.NotebookValidationError: 'metadata' is a required property
Failed validating 'required' in display_data:
On instance['cells'][20]['outputs'][0]:
{'data': {'text/html': '<style>.container { width:100% !important; }</style>',
'text/plain': '<IPython.core.display.HTML object>'},
'output_type': 'display_data'}
Exportando a formato PDF usando nbconvert con PDFviaHTML...
[NbConvertApp] Converting notebook /content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.ipynb to pdfviahtml
[NbConvertApp] ERROR | Notebook JSON is invalid: 'metadata' is a required property
Failed validating 'required' in display_data:
On instance['cells'][20]['outputs'][0]:
{'data': {'text/html': '<style>.container { width:100% !important; }</style>',
'text/plain': '<IPython.core.display.HTML object>'},
'output_type': 'display_data'}
[NbConvertApp] ERROR | Notebook is invalid after preprocessor <nbconvert.preprocessors.tagremove.TagRemovePreprocessor object at 0x7d4e28720220>
Traceback (most recent call last):
File "/usr/local/bin/jupyter-nbconvert", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.10/dist-packages/jupyter_core/application.py", line 283, in launch_instance
super().launch_instance(argv=argv, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/usr/local/lib/python3.10/dist-packages/nbconvert/nbconvertapp.py", line 420, in start
self.convert_notebooks()
File "/usr/local/lib/python3.10/dist-packages/nbconvert/nbconvertapp.py", line 597, in convert_notebooks
self.convert_single_notebook(notebook_filename)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/nbconvertapp.py", line 563, in convert_single_notebook
output, resources = self.export_single_notebook(
File "/usr/local/lib/python3.10/dist-packages/nbconvert/nbconvertapp.py", line 487, in export_single_notebook
output, resources = self.exporter.from_filename(
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/templateexporter.py", line 386, in from_filename
return super().from_filename(filename, resources, **kw) # type:ignore[return-value]
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 201, in from_filename
return self.from_file(f, resources=resources, **kw)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/templateexporter.py", line 392, in from_file
return super().from_file(file_stream, resources, **kw) # type:ignore[return-value]
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 220, in from_file
return self.from_notebook_node(
File "/usr/local/lib/python3.10/dist-packages/notebook_as_pdf/__init__.py", line 222, in from_notebook_node
html_notebook, resources = html_exporter.from_notebook_node(
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/html.py", line 268, in from_notebook_node
html, resources = super().from_notebook_node(nb, resources, **kw)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/templateexporter.py", line 408, in from_notebook_node
nb_copy, resources = super().from_notebook_node(nb, resources, **kw)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 154, in from_notebook_node
nb_copy, resources = self._preprocess(nb_copy, resources)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 355, in _preprocess
self._validate_preprocessor(nbc, preprocessor)
File "/usr/local/lib/python3.10/dist-packages/nbconvert/exporters/exporter.py", line 322, in _validate_preprocessor
nbformat.validate(nbc, relax_add_props=True)
File "/usr/local/lib/python3.10/dist-packages/nbformat/validator.py", line 509, in validate
raise error
nbformat.validator.NotebookValidationError: 'metadata' is a required property
Failed validating 'required' in display_data:
On instance['cells'][20]['outputs'][0]:
{'data': {'text/html': '<style>.container { width:100% !important; }</style>',
'text/plain': '<IPython.core.display.HTML object>'},
'output_type': 'display_data'}
Descargando los archivos generados...
Exportación y descarga completadas.
In [16]:
# Rutas del archivo del cuaderno y salidas generadas
notebook_path = \
"/content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.ipynb"
html_output_path = \
"/content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.html"
pdf_output_path_viahtml = \
"/content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim-pdfviahtml.pdf"
#pdf_output_path_pandoc = \
# "/content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim-pandoc.pdf"
# Exportar el cuaderno a formatos HTML y PDF
print("\nExportando a formato HTML...")
!jupyter nbconvert --to html "$notebook_path" --output "$html_output_path"
print("\nExportando a formato PDF usando nbconvert con PDFviaHTML...")
!jupyter nbconvert --to pdfviahtml "$notebook_path" \
--output "$pdf_output_path_viahtml"
#print("\nExportando a formato PDF usando Pandoc...")
#!jupyter nbconvert --to pdf "$notebook_path" --output "$pdf_output_path_pandoc"
# Descargar los archivos generados
print("\nDescargando los archivos generados...")
try:
files.download(html_output_path) # Descargar HTML
files.download(pdf_output_path_viahtml) # Descargar PDF generado con PDFviaHTML
# files.download(pdf_output_path_pandoc) # Descargar PDF generado con Pandoc
except Exception as e:
print(f"Error descargando los archivos: {e}")
print("\nExportación y descarga completadas.")
Exportando a formato HTML...
[NbConvertApp] Converting notebook /content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.ipynb to html
[NbConvertApp] ERROR | Notebook JSON is invalid: Additional properties are not allowed ('metadata' was unexpected)
Failed validating 'additionalProperties' in stream:
On instance['cells'][163]['outputs'][0]:
{'metadata': {'tags': None},
'name': 'stderr',
'output_type': 'stream',
'text': '[I 2024-12-19 13:22:34,041] A new study created in memory with '
'n...'}
[NbConvertApp] WARNING | Alternative text is missing on 19 image(s).
[NbConvertApp] Writing 7095636 bytes to /content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.html
Exportando a formato PDF usando nbconvert con PDFviaHTML...
[NbConvertApp] Converting notebook /content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim.ipynb to pdfviahtml
[NbConvertApp] ERROR | Notebook JSON is invalid: Additional properties are not allowed ('metadata' was unexpected)
Failed validating 'additionalProperties' in stream:
On instance['cells'][163]['outputs'][0]:
{'metadata': {'tags': None},
'name': 'stderr',
'output_type': 'stream',
'text': '[I 2024-12-19 13:22:34,041] A new study created in memory with '
'n...'}
[NbConvertApp] WARNING | Alternative text is missing on 19 image(s).
[NbConvertApp] Writing 12371924 bytes to /content/drive/MyDrive/Colab Notebooks/Proyecto_Final_DeteccionFraudeTarjetaCredito_ErikaSamaraAlvaresAngelim-pdfviahtml.pdf
Descargando los archivos generados...
Exportación y descarga completadas.